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A postdoctoral position in algorithms is available in the research group of Prof. Dr. Klaus
Jansen, Algorithms and Complexity at the Department of Computer Science of Kiel
University (CAU Kiel), Germany, within the research project

"Structural results and their application in scheduling and packing problems"

funded by the Deutsche Forschungsgemeinschaft (DFG).
The position is for 3 years (TV-L 13) starting 1st September 2017 (the exact date is
negotiable).

The main research focus of the DFG project is to prove structural results for integer linear
programs and to design FPT algorithms and approximation algorithms
for bin packing and scheduling on identical machines. Therefore, the applicant should have a
strong interest in Theoretical Computer Science, particularly in Algorithms.

Applicants should hold (or should be close to obtaining) a PhD degree in Computer Science
or Mathematics, and should have a solid publication record in the area of algorithms and
complexity. Top conferences in this area include STOC, FOCS, SODA, IPCO, ESA, ICALP,
and STACS.

Kiel University aims at increasing the number of women in research and academic teaching
and strongly encourages applications of accordingly qualified women. Women will be
preferred, provided equal qualifications and scientific performance.
The CAU supports the employment of severely disabled persons. Therefore, severely
disabled persons will be preferred, provided equal qualifications and scientific performance.
Applicants with a migration background are particularly welcomed.


The application material should include (1) a cover letter describing the interest in the
position, (2) a CV and a list of publications, (3) copies of university certificates and (4) the
names and contact information of at least two references. The material should be sent
electronically until July 14, 2017 to Klaus Jansen under .


Prof. Dr. Klaus Jansen
Department of Computer Science
Technical Faculty of the
Christian-Albrechts-University of Kiel
Christian-Albrechts-Platz 4
24118 Kiel, Germany.
https://www.algo.informatik.uni-kiel.de/

We expressly refrain from asking for photographs, therefore we ask you not to send a photo
with your application.

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At UNSW Sydney (Australia) we have an opening for up to two UNSW Scientia
PhD scholarships within the project Algorithms for Scheduling and
Cooperation in Distributed Energy Markets. The project is to design and
implement modern algorithms (parameterized algorithms and approximation
algorithms) for problems around clean energy technology adoption. The UNSW
Scientia PhD scholarships are very generous scholarships covering the
tuition fees, a stipend of AUD 40,000 per year, and a research support
package of AUD 10,000 per year.

Supervisory team: Serge Gaspers, Iain Macgill, and Aleksandar Ignjatovic.

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The UNSW Scientia PhD Scholarship Scheme (package of A$50,000 per annum)

Topic - Smart Mobility Methodologies for Optimizing Transport Networks

With increases in communication technology and wider availability of diverse real-time data, the behaviour of travellers is fundamentally shifting as individuals are more open to shared mobility, journey adaptation, and trip-based pricing. Further, the tools available to regional planners are growing exponentially in terms of predictive capability as well as complexity.

The successful applicant will develop optimization-based mathematical theory and models for improving transport system congestion and reliability, as well as practical and efficient solution methods and tools. They will deliver research advances on system-wide transport descriptors, such as dynamic transport network equilibria and the mechanisms governing traveller behaviour, and develop methods to improve the operation of the transport network.

Supervisory team
----------------
* Prof Travis Waller - School of Civil and Environmental Engineering
* Dr Lauren Gardner - School of Civil and Environmental Engineering
* Prof Gary Froyland - School of Mathematics and Statistics

Further details
===============

The UNSW Scientia PhD Scholarship Scheme aims to attract the best and brightest people into strategic research areas and provide them with an enhanced culture of research excellence, mentoring, career development, leadership and community. The Scheme provides 4 years of funding for the candidate to complete a PhD at UNSW. The awards will be made across all Faculties in areas of identified research strengths and the scheme will ensure that candidates reflect the disciplinary, gender and cultural diversity of UNSW. In contrast to our other prestigious UNSW scholarship schemes, the UNSW Scientia scheme is targeted and applicants must apply to a specific research area with an identified supervisory team.

Distinctiveness - The key distinctive features of the UNSW Scientia PhD Scholarship Scheme include:

1. Prestigious Scholarship Package - UNSW Scientia PhD scholars are awarded a scholarship package of $50,000 per annum, comprising a tax-free living allowance of $40,000 per annum for 4 years, and a support package of up to $10,000 per annum to provide financial support for career development activities
2. Outstanding supervision combined with exceptional research environments - all successful candidates will be supervised by teams of UNSW's best researchers aligned with UNSW research strengths, who will be selected on the basis of their demonstrated capacity in excellence in research and high quality supervision, plus their commitment to developing and mentoring PhD scholars
3. Engagement - all successful candidates will have a strong commitment to making a difference in the world with demonstrated potential for contributing to the learning and teaching excellence, social engagement and/or global impact pillars of the 2025 strategy
4. Career and Development - all successful candidates will be provided with career coaching and development throughout the 4 years of candidature, and encouraged to engage with knowledge exchange activities.

Eligibility
===========
1. The scheme is open to domestic and international candidates
2. To be able to apply to the scheme, potential applicants must be nominated by a team of approved UNSW supervisors
3. Due to the structured program, only new PhD candidates qualifying for direct entry to the PhD will be eligible for entry into the scheme
4. The minimum requirement for admission to the Scheme is:
5. an appropriate four year degree of Bachelor with at least upper second class Honours, or
6. a completed Masters by Research including a substantial research component, or
7. qualifications at a level considered equivalent to either of the above
8. All nominees must be eligible for admission to the PhD program as described in the Admission to Higher Degree Research Programs Procedure
9. Successful candidates should be able to meet all of the selection criteria (listed below) at the highest level
10. All successful applicants must be located on a UNSW campus for a minimum of 2 years and this must include the first year of the program.

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Two post-doc positions in Combinatorial Optimization Group, Institute of Computer Science, University of Wroclaw.

One position is within the project “Algorithmic fundamentals of supply chain networks” led by Jarosław Byrka. It aims at developing algorithmic tools for typically NP-hard optimization problems in network design such as routing or clustering.

One position is within the project “Algorithmic online optimization for graph problems” led by Marcin Bienkowski. It aims at developing online algorithms for efficient dynamic placement and leasing of resources in networks.

Both post-doc positions are intended for 12 months starting from October 2017. The starting time and duration are flexible. It is possible to apply for both projects and indicate the preferred one.

Our group offers a stimulating working environment, see: http://ii.uni.wroc.pl/badania/zaklad/ZOK

Wroclaw is a modern city, pleasant to live in, with over 100 000 students. Wroclaw was a European Capital of Culture in 2016, see: http://www.wroclaw2016.pl/, and this year it will host The World Games, see: https://theworldgames2017.com/en/.

Applications should be submitted by July 11, 2017. We aim at selecting the candidates till the of July.

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Positions in Machine Learning and Operational Research are available, within the PhD Programme in Computer Science (total of 10 positions available).

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Information Structures in Complex Queueing Systems

Strategic decisions in queueing systems are very common in everyday life. Rooms in hospitals, voting locations, security gates at international airports and amusements parks are many examples of systems for which strategic decisions are taken by each customer in order to join optimally those systems [1].

The analysis methods and techniques for studying such decision processes in complex queueing systems are mainly described in the book [2]. An interesting new aspect of these problems is to study the impact of the information structure onto the strategic decisions and equilibrium of the customers. Information about the system like the actual queue length and/or processing rate is an important parameter for customers who face the decision whether to join or to choose a particular option/system. Moreover, customers are not all equal faced to the information proposed. Some of them can have easily access to the information and some not. Even, the system manager may aim to hide some information to customers. A recent paper [3] has shown that some simple policies like giving all the information or nothing are optimal in some particular cases.

The aim of this project is to extend those results for more complex structure of the system.

Backgrounds: The candidate should have good knowledge in Mathematics, stochastic models and optimization techniques. Some basic skills in programming and simulations are also welcomed.

Starting: September 1^st 2017.

Location: University of Avignon, LIA, France.

Supervisor: Professor Y. Hayel, CERI/LIA University of Avignon, France. yezekael.hayel@univ-avignon.fr

[1] H. Rafael, R.-G. Ricky, Equilibrium in a two dimensional queueing game: When inspecting the queue is costly, Working paper.
[2] R. Hassin, M. Haviv, To queue or not to queue: Equilibrium behavior in queueing systems, Vol. 59, Springer Science & Business Media, 2003.
[3] E. Simhon, Y. Hayel, D. Starobinski, Q. Zhu, Optimal Information Disclosure Policies in Strategic Queueing Games, in Operation Research Letters, vol. 44, no. 1, 2016.

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A fully-funded 3-year PhD scholarship, funded by the University of Strathclyde Strategic Technology Partnership with Capita, is available with a start date of October 2017. The project, entitled "A Mixed-Methods Approach for Clinical Triage to Improve the Patient’s Journey and System Performance", will be under the supervision of Prof. Alec Morton and Dr. Kerem Akartunali. All applications need to be submitted by 30 June 2017.

The scholarship will cover a fee waiver at Home/EU rate and annual stipend of £14,510. International students may also apply, however, they will need to demonstrate further funding to cover the difference between international and Home/EU fees (approximately £10k per year). In exceptional cases, further funding may be also secured for excellent international students, however, this is not guaranteed.

The PhD project requires a numerate graduate with interests in healthcare. Candidates should have at least a strong Honours degree or equivalent (a strong 2:1 Honours degree, or a B.Sc. degree with 3.3 GPA in a 4.0 system), or preferably a Master’s degree, in a relevant discipline such as management science, industrial engineering, operations research, mathematics or computer science (amongst others). Experience/knowledge in relevant MS/OR techniques is not essential but desirable. Candidates who are not native English speakers will be required to provide evidence for their English skills (such as by IELTS or similar tests that are approved by UKVI, or a degree completed in an English speaking country).

Project details are provided below. For queries regarding the project, please contact Prof. Alec Morton and Dr. Kerem Akartunali.

PROJECT DESCRIPTION

Clinical triage has been an increasingly popular area of research in the operations management, analytics and operational research (OR) communities due to its significant impact on the downstream health system. A patient’s journey starts in the system with triage, and patient data including basic demographics, presenting complaint and physiology (heart rate and blood pressure) influence ongoing management and resource utilization. Decions made at triage have significant impact on the hospital system, since the triage category directly influences the location in which patients are seen, and therefore, a system under stress fails to achieve KPIs, resulting in a degradation in patient care.

Optimising patient flow within the Emergency Department (ED) requires an integrated approach to patient management that extends beyond the ED back to the community following treatment within the hospital. Triage is the landfall moment in a patient’s journey, where their condition requires the resource intensive (and costly) management within a hospital rather than community based services.

In this project, the main objective is to better understand patient flows and build a decision support tool that integrates various OR tools in the most effective way in order “to better understand how a patient’s journey can be determined and influenced by enhanced processes at triage.” How data generated at triage can be linked to the patient’s record to create sophisticated predictive modelling of the patient’s journey and resource requirement during the episode of care within the hospital and broader healthcare system. The project will involve a continuous collaboration with external clients Capita and local NHS hospitals for the purposes of data collection and implementation/experimentation. We consider in particular three OR toolboxes in order to design and implement such a mixed-methods framework:

1) Simulation will enable us to build models that will be able to evaluate what-if scenarios when the system involves various uncertainties.
2) Optimization will enable us to build models that can make the most out of the system while taking into account its limitations and constraints.
3) Multi-Criteria Decision Analysis will enable us to encounter the often contradictory preferences of various stakeholders of the system.

These toolboxes will be integrated to each other in the final framework in order to minimize their own limitations.

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We are offering a 3-years PhD thesis in Operations Research on "Optimal
planning of wireless networks". This student will work 80% of the time at
Orange labs, Paris, and 20% of the time at the Laboratoire d'Informatique
d'Avignon. The net salary is rough 2000€ per month.

We are looking for an outstanding student with good programming skills, a
good level of Operations Research, and some interest in networks (knowledge
in robust optimization or stochastic programming is a plus). The interested
students must send their CV (including marks, ranking, and recommendation
letters) to rosa.figueiredo@univ-avignon.fr. Knowledge of French is not
necessary but would ease the integration of the student at Orange labs.

The thesis will be co-advised by Matthieu Chardy, Rosa Figueiredo, Adam
Ouorou, and Michael Poss. More information on the topic of the thesis can
be found here https://orange.jobs/jobs/offer.do?joid=61998&lang=FR

Please contact rosa.figueiredo@univ-avignon.fr before applying on the
website.

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PhD studentships for UK and EU nationals are available in "Statistics and Operational Research for Industry" (STOR-i), Lancaster University, UK, for October 2017 entry.

STOR-i is a pioneering 4 year MRes/PhD programme established in 2010. Using industrial challenges as the catalyst for mathematical innovation, the centre develops future international research leaders. Industrial partners include AstraZeneca, ATASS, BT, IBM, Rolls-Royce and Shell. Our PhD projects involve substantial collaboration with our industrial partners, providing a unique research and training experience. The cohort-based PhD programme offers a unique style of PhD training, which provides an opportunity to:

- be part of an exciting new group of researchers;
- co-operate and interact with like-minded peers;
- work directly with leading industry partners;
- open up real potential for rapid career progression;
- make significant scientific and industrial impact with your research.

The studentships include tuition and a tax-free enhanced maintenance grant of approximately £16,296. On successful completion of the MRes, this will rise to £17,296 per year, or £19,296, for students undertaking an industry funded PhD.

Due to demand for places, early applications are strongly encouraged. To find out more please visit http://www.stor-i.lancs.ac.uk/apply or contact us at stor-i@lancaster.ac.uk.

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The Operations Research group of the University of Antwerp (ANT/OR) is
looking for a PhD candidate in the areas of optimization and statistics.
This position is for 4 years as a full-time doctoral researcher, and it is
framed within a project financed by the Flemish Fund for Scientific
Research (FWO).

The topic of the project is related to the performance analysis of
(meta)heuristic optimization algorithms. The main objective is to develop a
new methodology for evaluating and fine-tuning the execution of these
algorithms. Such methodology will be based on well-founded statistical
principles, and will constitute the core of an open-source software
package. The ultimate goal is to help researchers/practitioners to develop
better optimization algorithms in shorter periods of time.

The ideal candidate has a master’s degree in computer science, mathematics,
statistic, civil/business engineering or other related field of study.
He/she has a strong background in operations research and statistics,
excellent programming and software development skills, and a good command
of the English language. Previous experience with (meta)heuristic
algorithms and statistical modeling/analysis is highly valued.

The successful candidate will receive a tax-free scholarship (of
approximately 1800 euros per month) for a 4-year period, subject to yearly
evaluation. He/she will enroll in the doctoral program of the Faculty of
Applied Economics (Department of Engineering Management) and is expected to
obtain a PhD degree by the end of the fourth year. There is a sizeable
budget available for attending international conferences, advanced
workshops and other courses. Although the focus of the position is mainly
on research, the candidate might be asked to take part in some teaching
activities.

The ANT/OR research group is a young and
dynamic team of about 13 researchers. It focuses on developing optimization
methods to solve decision-making problems in a wide variety of fields,
ranging from logistics and supply chain, transportation and production
planning, to humanitarian logistics and digital music. The group works in
close collaboration with several industrial partners, non-profit
organizations and other international research institutions. The University
of Antwerp is home to around 20000
students, of which 18% come from overseas. It has been ranked 13 in the top
150 young universities in the world (with less than 50 years of age).
Antwerp is the second largest
city in Belgium. It has one of the busiest ports in Europe and is one of
the biggest diamond trade centres in the world. This vibrant city is famous
for its history, fashion industry, delicious food (beer, chocolate, fries,
waffles, ...) and excellent quality of life.

Interested candidates are requested to provide a motivation letter and a
detailed CV. In order to apply for this position, please send these
documents (in PDF format) by email to Daniel Palhazi Cuervo (
daniel.palhazicuervo@uantwerpen.be) no later than July 1st, 2017. The
expected starting date is October 1st or sooner, if possible.

For more information about this position, you are welcome to contact Daniel
Palhazi Cuervo (daniel.palhazicuervo@uantwerpen.be) or prof. Kenneth
Sörensen (kenneth.sorensen@uantwerpen.be).

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We invite applications for a fully funded PhD position in Operations Research and Machine Learning. The PhD should start no later than October 2017.

Title: demand models and schedule design for airlines

Operations Research has been successfully optimizing airline industry processes for more than sixty years. The topic of "schedule design" provides tools to decide which market to serve, with what frequency, and how to schedule flight legs to meet these frequencies. Mathematically, this requires to model demand and customer preferences, and then find the set of flight legs to operate that maximizes the predicted profit.

Two recent trends challenge the traditional approaches to schedule design. First, the air transportation industry moved from a monopolistic to a very competitive environment, which calls for a new generation of demand models. Second, the amount of data made available by information technologies opens new opportunities to better take into account the uncertainty of demand. These new stochastic models notably enable to evaluate and control risk when optimizing the flight legs schedule.

The objective of this PhD is to set up new approaches to schedule design that responds to these challenges.

* Research supervision and environment
Scientific supervisors: Frédéric Meunier and Axel Parmentier

This PhD takes place in the context of the Operations Research and Machine Learning chair between Air France and the Ecole des Ponts Paristech. The candidate will share its time between the Cermics, the applied mathematics center of the Ecole des Ponts Paristech, and the Operations Research department of Air France.

Ecole des Ponts Paristech is one of the top French universities, and Paris provides a startling research environment in mathematics and computer science.

* Candidate profile
Students are expected to carry out top-level research. They will develop efficient solution algorithms to difficult planning problems. They must have a master degree in Operations Research, Applied Mathematics, Computer Science, or a related field. They should demonstrate good programming skills. Knowledge of mathematical programming is required, experience in stochastic optimization, machine learning, or game theory is an asset. Even though they may learn French during their stay, French knowledge is not mandatory.

* Application procedure
Interested candidates should send their application package electronically to Axel Parmentier (axel.parmentier@enpc.fr). The application should contain the following documents:
- Cover letter explaining the motivation for the PhD, your research/career interests, as well as your preferred starting date
- Detailed curriculum vitae with list of publications (if any)
- Grade records of Bachelor and Master programs
- Reference letters or contact details of references (one of them should be the supervisor of the candidate's Master studies)

Applications are opened from now. The position is limited to three years. If you have further questions regarding this position, please contact Axel Parmentier (axel.parmentier@enpc.fr).
Position will remain available until filled.

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A PhD position in Operations research is available at INRIA Lille (France) in the INOCS team : https://team.inria.fr/inocs/ and Ecole Polytechnique de Montréal



Title : A Leader-Follower Framework for Demand Response
Keywords Optimization, operations research, Energy

The PhD thesis will be done according to a co-tutelle agreement between Ecole Polytechnique de Montreal and Centrale Lille. It will be partly supported (50%) by Canada

Scientific supervisors: Luce.Brotcorne@inria.fr , frederic.semet@inria.fr , miguelf.anjos@polymtl.ca

To apply, please send a cv and a motivation letter to the scientific supervisors

Electricity is a critical source of energy for our society. The smart grid encompasses the developments concerning the power system that are taking place, or need to take place, for the system to better meet the needs and expectations of society in the 21st century. However it also brings up new challenges to operate the resulting system. These include using the power grid already in place more efficiently, integrating renewable energy sources such as wind and solar power generation, managing the flows of power and of information, and integrating loads as active participants in the grid operations.

A central challenge in the full implementation of the smart grid is the effective integration of the customers as active participants in the grid, a process generally referred to as demand response. This includes every means available to support an active role of the loads in shaping the load curve of the grid: peak clipping, valley filling, load shifting, strategic conservation, strategic load growth, and flexible load shape. Although direct load control can be effective to handle a small number of large loads, it is certainly not practical for managing demand response in the smart grid of the future.

The objective of the proposed research is to design and implement a new framework to ensure the economic efficiency of the provision of demand response . This research is set in a context where companies providing energy via the power grid, such as utilities and virtual power plants, can use dynamic electricity pricing to incentivize their customers to shift all or part of their loads from the peak periods to off peak periods so as to adjust the load curve in a way that facilitates the operation of the power grid.

We will capture the economic aspects of this context using a leader-follower framework . This is a wellknown game-theoretic framework applicable to situations where there are two decision levels with conflicting objectives. In its simplest form, there are two players, namely the leader and the follower. The leader and fully controls a set of variables that represents its decisions, and the follower solves an optimization problem according to its own interests, but taking the leader's decisions as given. The resulting model is a bilevel optimization problem.

Bilevel optimization is a difficult class of problems; indeed all leader-follower frameworks lead to intrinsically difficult problems. Thus it is imperative to take exploit the characteristics of the speciffc application to formulate and solve these problems.

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We invite applications for fully funded Ph.D. positions in Montreal in the
domain of Operations Research, as well as in the intersection between
Operations Research and Machine Learning. These positions aim at advancing
our collective capabilities to solve hard optimization problems and our
understanding of how external data sources and real-time data can be used
to improve the decision-making process. Earliest starting dates are
September 2017 and January 2018.

* Research Topics
Research topics may be aligned with the interests of the students. Several
pre-defined research projects are also available (some of them in
collaboration with industrial partners). Topics may generally include the
following:
- Large-scale optimization for dynamic (combinatorial) planning problems
(e.g. Benders decomposition, Column Generation, Lagrangian Relaxation)
- Optimization under uncertainty (e.g. stochastic programming, robust
optimization, improvement of uncertainty sets and scenario generation
through external data)
- Improvement of (à priori and real-time) planning through integration of
external data sources via machine learning
- Development of Operations Research based algorithms to efficiently solve
machine learning problems

Research may be fundamental, applied, or a mix of both. Applications may
include domains such as Logistics and Transportation (e.g., vehicle sharing
systems, facility location, traffic management), Telecommunications and
Revenue Management (e.g., assortment planning).

* Research supervision and environment
The students will work under the supervision of Dr. Sanjay Dominik Jena,
professor at the Department of Management and Technology (
mantech.esg.uqam.ca) at the Management School of Université du Québec à
Montréal (École des Science de la Gestion, UQAM). Sanjay Dominik Jena is
also member of the worldwide renowed, Montreal-based research center
CIRRELT (www.cirrelt.ca) and the Canada Exellence Research Chair in Data
Science for Real-time Decision-making (www.cerc-datascience.polymtl.ca).
Students may be co-supervised by other members of these research centers
and will be registered either at the joint Ph.D. program in Management
(including ESG UQAM, HEC Montréal, McGill and Concordia) or at the
respective university of the other supervisor(s).

Montreal is a dynamic Metropolis, located in the province of Québec,
Canada. The city is bilingual (French/English) and known for its
international atmosphere. Montreal is known for its vibrate research
environment, hosting five major universities and several research centers
in the domains of Operations Research and Machine Learning. The successful
candidates will be working in one of the above-mentioned research centers.

* Candidate profile
Students are expected to carry out top-level research. They will develop
efficient solution algorithms to difficult planning problems such as those
in the above-listed domains. Candidates should have a Masters degree (or
equivalent) in Computer Science, Operations Research, Applied Mathematics,
or a related field. They should demonstrate good programming skills.
Experience in mathematical programming, machine learning, or data analysis
is an asset. Candidates should possess a good level of written and oral
English. Even though they may eventually learn French during their stay,
French knowledge is not obligatory.

* Application procedure
Interested candidates should send their application package electronically
to Sanjay Dominik Jena (sanjay.jena@cirrelt.ca). The application should
contain the following documents:
- Cover letter explaining the motivation to perform a Ph.D. in one of the
domains mentioned above in Montreal, your research interests, as well as
your preferred starting date
- Detailed curriculum vitae with list of publications (if any)
- Grade records of Bachelor and Master programs
- Reference letters or contact details of references (one of them should be
the supervisor of the candidate's Master studies)

If you have further questions regarding these positions, possible research
topics, etc., please contact Sanjay Dominik Jena (sanjay.jena@cirrelt.ca).
Positions will remain available until filled.

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The research group in Health Care Management of the School of Mines in Saint-Etienne is seeking a PhD candidate in Data Science and/or Industrial Engineering and/or Operational Research and/or Computer Science

Subject Title : Medical decision analytics using health data: application to the lung cancer case study
Supervisors: Vincent Augusto, Xiaolan Xie and Raksmey Phan
Structure: Mines Saint-Etienne, UMR CNRS 6158 LIMOS
Location: 158 cours Fauriel 42023 Saint-Etienne cedex 2 France
Starting date: October 1 st 2017

Subject
Data analytics consists in developing optimization and/or machine learning based algorithms that learn
to recognize complex patterns within valuable and massive data. Challenges related to that topic are
numerous, and many scientific fields are involved: computer science, data science, operational
research, process and data mining.
When applied to health-care, the objectives are often summarized as improving quality and timeliness
of care, maximizing financial performance, and decreasing practice variability across organizations. It
relies on the following tasks: (i) identify critical features that impact outcomes (allocation of limited
resources/time for greater effect); (ii) seek greater use of treatment evidence to advance the quality
and effectiveness of care delivery; (iii) rapid learning and best practice dissemination. Process mining
is also a closely connected field of research (van der Aalst 2004).
The main objective of this thesis consists in developing innovative optimization and machine learning
techniques to aid medical decision using available health databases such as PMSI (Programme de
Médicalisation des Systèmes d'Information, national database of hospital stays in France), SNIIRAM
(Système national d'information inter-régimes de l'Assurance maladie, national database of the health
insurance) and local databases of the CLB (Centre Léon Bérard, hospital specialized in cancer treatment
in Lyon, France). In a previous work (Prodel 2017), the application of classification methods such as
decision trees or random forest have proven very effective to predict the clinical pathway of patients
using a set of medical features. The same approach can be applied to the prediction of medical acts
within a hospital stay, for example: “Depending on his/her medical history, can an obese patient having
a severe heart condition have a heart surgery to implant a defibrillator?” or “Depending on the stage
of the lung cancer, what is the treatment among chemotherapy, radiotherapy and surgery that
optimize the survival rate of the patient?” This problem is partly related to the tuning of parameters
used to build decision trees (Camilleri et al. 2014; Coroiu 2016).

Particle swarm optimization techniques for feature selection, coupled with an optimization-based
discriminant analysis model (DAMIP) is an emergent and promising field of research when applied to
identify a classification rule with relatively small subsets of discriminatory factors that can be used to
predict resource needs, outcome for treatment... For example, (Lee et al. 2012) proposed a clinical
decision tool for predicting patient care characteristics and demonstrated that optimization achieve
better result than classical machine learning techniques. Such approach was also used for modeling
and optimizing clinic workflow (Lee et al. 2016).The scientific challenge of this thesis is twofold:
- Propose a theoretical research to develop new algorithms combining optimization and
classification methods applied to medical decision making, taking into account the special
features of the diagnosis related groups in France and in Europe. To do so, we will capitalize
on the short advance the I4S laboratory have on (i) research in the application of optimization
to predict patient clinical pathways by combining data/process mining, operational research
and machine learning and (ii) knowledge of health-care databases in France and in UK (through
the emerging collaboration with NHS and Westminster University, London, UK).
- Develop a comprehensive testbed experiment to assess the proposed algorithms on a lung
cancer case study. Several level of discovery will be used: hospital databases (PMSI), health
insurance databases (PMSI+SNIIRAM) and finally hospital databases (PMSI+SNIIRAM+Centre
Léon Bérard database) to understand the amount of required data needed to aid the medical
decision. To the best of our knowledge such case study has never been investigated in the
literature.

National databases such as PMSI and SNIIRAM are already available for such study, and a collaboration
with the Centre Léon Bérard (Lyon, France) is already live to access more detailed data, with the
support of the I-Care cluster (Lyon, France) for dissemination of ongoing research on big data and
healthcare.

Profile
The candidate should have strong background in data science (machine learning, process mining, data
mining, artificial intelligence), industrial engineering (formal modelling, flow simulation, performance
evaluation), operational research (mathematic modelling, optimization) and computer science (coding
C/C++/Python, Java, knowledge on mathematical libraries, statistics, optimization).
Send a CV, motivation letter, marks of the 3 previous years (including current one) to augusto@emse.fr
in order to schedule an interview.

References
(Camilleri et al. 2014) M. Camilleri, F. Neri, and M. Papoutsidakis. “An algorithmic approach to parameter selection in machine learning using meta-optimization techniques”. WSEAS Transactions on Systems, 13:202–213, 2014.
(Coroiu 2016) A. M. Coroiu. “Tuning model parameters through a genetic algorithm approach.” In IEEE
12 th International Conference on Intelligent Computer Communication and Processing (ICCP), pages
135–140, Sept 2016.
(Lee et al. 2012) Lee EK, F Yuan, DA Hirsh, MD Mallory and HK Simon. “A clinical decision tool for
predicting patient care characteristics: patients returning within 72 hours in the emergency department”, AMIA Annu Symp Proc., 495-504, 2012
(Lee et al. 2016) Lee EK et al. “Systems Analytics: Modeling and Optimizing Clinic Workflow and Patient
Care”, In Healthcare Analytics: From Data to Knowledge to Healthcare Improvement, chapter 9, 2016
(Prodel 2017) M. Prodel. “Process discovery and simulation of clinical pathways using health-care
databases”. PhD Thesis, 2017.
(van der Aalst 2004) Wil M van der Aalst. “Workflow mining: Discovering process models from event
logs”. Computers in industry, 16:1128–1142, 2004.

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The Centro de Investigacion y de Estudios Avanzados (Center for Research and
Advanced Studies, Cinvestav after its name in Spanish), which is the top
public research center in Mexico, has two positions for full-time
researchers in Computer
Science to be filled for the Computer Science Department located at Mexico
City.

Candidates are required to have a PhD in Computer Science (or a related
field) and an outstanding research record, or a very good potential to
develop an outstanding
research career. Candidates from all areas of computer science are welcome
to apply.

Both junior and senior researchers are encouraged to apply for a position.
The type of appointment in turn could be as an Assistant or as an Associate
Professor.
The salary is competitive and varies depending on the curriculum of the
candidate (note that research experience is emphasized over teaching
experience).

The candidate is expected to have a light teaching load (one graduate-level
class per term), so that he/she can concentrate mostly on research
activities. Candidates do not need to be fluent in Spanish, as they can
teach in English.

CINVESTAV-IPN is the most important public research center in Mexico, with
645 full-time researchers and the second highest scientific productivity
(per researcher) of the country
(only after UNAM, which is the largest university in Mexico). The Computer
Science
Department offers both a MSc and a PhD program in Computer Science
(CINVESTAV-IPN only offers graduate programs). Both programs are included
in the National Graduate Program certification from CONACyT, which means
that all our Mexican full-time students receive
a stipend scholarship from the Federal Government (no tuition is required
for Mexican students).

The Computer Science Department of CINVESTAV-IPN currently has 17 full-time
researchers
and about 70 graduate students, and has research projects on Computer
architecture, Cooperative work, Cryptography, Databases, Evolutionary
computation, Optimization, Super computing and Visualization.


Interested candidates must send (via email to: jobs2013@cs.cinvestav.mx)
the following
information (as a single PDF file):

1) A detailed curriculum vitae that clearly indicates the publications in
international journals (highlighting the papers in journals included in the
Journal Citations Report), and a list of MSc and PhD graduates (if any).
The candidate should also include a copy of the three papers that he/she
considers as the most representative of his/her research. Note that papers
published in the Lecture Notes series from Springer will not be considered
as JCR papers.

2) A research statement that briefly (no more than 1 page) describes the
sort of research that the candidate has done so far and his/her research
plan in case of being hired.

3) A teaching statement that briefly (no more than 1 page) describes the
sort of graduate-level courses (in computer science) that the candidate can
teach (and has taught in the past, in case of having teaching experience).

4) The names and contact information (postal address, phone and fax number,
and email) of 3 references.

The screening of candidates will start at the end of June and the position
is expected to be filled by the end of 2017.

For further information, please contact:

Dr. Francisco Rodriguez-Henriquez
Chair, Computer Science Department
CINVESTAV-IPN
Av. IPN No. 2508
Col. San Pedro Zacatenco.
07300 Mexico City
MEXICO
Tel: +52 (55) 5747 3800 x 6564
Fax: +52 (55) 5747 3757
URL: http://www.cs.cinvestav.mx
email: francisco@cs.cinvestav.mx

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The Department of Informatics of the Faculty of Science at the University of Mons (Mons, Belgium) invites applications for a tenure-track faculty position in computer science, with special emphasis on big data analytics. The position is at the level of «chargé de cours» (assistant professor). The expected starting date for the position is October 1, 2018. Qualified candidates must have a doctorate in disciplines related to computer science or computer engineering, and should have a strong commitment to teaching and an outstanding research record in applied aspects of big data analytics. The selected person should collaborate with existing research groups of the department and be active in fundraising for industrial and governmental projects.

Candidates should be able to fulfil teaching duties in the French language after two years. Teaching duties are on both the undergraduate and graduate level, and include novel courses in the candidate’s domain of expertise.

The University of Mons is a university located in the French community of Belgium with about 8000 students. Information about the Department of Informatics can be found at http://informatique.umons.ac.be/.

Applications should be sent by email to Prof. Dr. Véronique Bruyère (veronique.bruyere@umons.ac.be), Department Head. We encourage candidates to apply from now on. Applications must be submitted as PDF files, which include a cover letter, curriculum vitae, a one-page statement of teaching interests, a one-page statement of research interests in big data analytics, and names and contact information of at least three references. Following their application, candidates will be informed in due time about the further application procedure.

The most up to date information about the position and the application procedure will be maintained at http://informatique.umons.ac.be/job_offers/.

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The Master Program in Operations Research, Combinatorics and Optimization is one of the possible specializations for the second year of the Master of Science in Computer Science (semesters 3 and 4). The courses are taught in English.

Scientific objectives:
•Study of advanced and efficient methods and tools of Operations Research, Combinatorics and Optimization (Mathematical programming, Graph theory, Complexity theory, Stochastic programming, heuristics, approximation algorithms, Robust optimization...)
•Emphasis is on the use of these methods to implement efficient solution techniques to complex industrial applications (in supply chain management, scheduling, transport …)
•Preparation for research positions (in industry and academia)

Professional objectives:
At the end of the program, the students should be able to pursue a career in research (academic or industrial PhD), or to join major research and development departments or consulting companies in optimization. They might also build upon their ability to analyze operational problems methodologically to join less specialized companies and act as key actors in performance management: either by interfacing with consulting firms or by developing in-house solutions. In the long run, students who are moving towards industrial careers, strengthened by their experience in improving business performances and by the development of business-specific knowledge, should naturally reach decision-making positions with a high level of responsibility

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Applications are invited for aPostdoctoral Researcheror aPhD studentat the Department of Production and Logistics of the University of Wuppertal, Germany. The position is full-time and can be started as soon as possible for the applicant. The position is funded for three years (salary according to level E13).
We have a research focus on operations research methods with application in logistics, production planning and supply chain management. Applicants should have a business administration and/or theoretical/mathematical background and an interest in at least one of the following topics:
- operational research
- auction theory
- computational complexity theory
- scheduling theory
Postdoctoral researchers from other fields may apply as well, but should be prepared to detail how they fit in the research profile of the group.
All applicants should have an excellent first academic degree in computer science, mathematics, business administration or a related discipline.
Applications should include a detailed CV, a copy of master (or PhD) thesis, a brief statement of research interests, and a list of publications (if applicable).
Please send the application not later than June 13th, 2017 to

Bergische Universität Wuppertal,

Fakultät für Wirtschaftswissenschaft - Schumpeter School of Business and Economics,

Lehrstuhl für Produktion und Logistik,

Herrn Prof. Dr. Dirk Briskorn, 42097 Wuppertal

and via email to briskorn@uni-wuppertal.de

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A PhD position is available at TU Berlin, in the fields of discrete and robust optimization, for a start at the earliest possible date. The project, entitled "Robust Optimization of Load Balancing in the Operating Theatre", will be under the supervision of Guillaume Sagnol at the COGA Group of the Technical University of Berlin.

* Working field:
Working in the ECMath Junior Research Group "Optimization under Uncertainty“ and the project
"Robust Optimization of Load Balancing in the Operating Theatre". Further own research in the field of discrete and
algorithmic mathematics.

* Requirements:
Successfully completed university degree (Master, Diplom or equivalent) in Mathematics, grades clearly
above average desired; deep knowledge in combinatorial optimization and/or optimization under uncertainty; strong
interest in mathematical research; experience in scientific programming (e.g. Matlab, python, julia,...)

We welcome highly motivated candidates to send their electronic application per email to sagnol@math.tu-berlin.de (deadline for the application: 09.06.2017).

For further information do not hesitate to contact Guillaume Sagnol.

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Assistant Professor position in Systems, Control, and Optimization at IMT Lucca.

IMT School for Advanced Studies Lucca invites expressions of interest for an Assistant Professor position (“Type-A Ricercatore”) to carry out research in the areas of systems, control, and optimization. Research experience is expected in one of the following areas: control systems, numerical optimization, systems identification, machine learning. Research experience in model predictive control or convex optimization will be considered as a plus.

Candidates must have an excellent record of high-impact international publications and demonstrate enthusiasm for performing research. Activities include: research, tutorship and mentoring of PhD students, graduate teaching. The successful candidate will be part of the research unit Dynamical Systems, Control and Optimization (http://dysco.imtlucca.it/) at IMT Lucca.

The position is for 3 years, extendable for further 2 years. The indicative starting gross salary is EUR 34.898/year, net income may vary depending on income taxes, local taxes, retirement plan, health care deduction and tax exemptions. New employees who have worked in research-based positions abroad for the past two years may be eligible for a substantial tax rebate for the first three fiscal years of employment.

IMT School for Advanced Studies Lucca (http://www.imtlucca.it) is a public graduate school and research institute, ranked #1 among all public Italian universities for quality of research in the last national research evaluation. IMT is an interdisciplinary research environment, blending scientific competencies of management science, engineering, computer science, neuroscience, physics, and management of cultural heritage, striving to reach the fusion of theoretical comprehension and practical relevance in concrete applications. The working language at IMT Lucca is English.

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One year research position at a postdoctoral level (with a possible one year extension) is available at Algorithms research group (http://www.uib.no/rg/algo/), University of Bergen, Norway.

The successful candidate will work on "Multivariate Algorithms: New domains and paradigms" project funded by the Norwegian Research Council and led by Fedor Fomin. The objective of the project is to develop new theory of multivariate algorithms as well as to apply the new theory to fundamental algorithm design paradigms.

Candidates are required to have completed (or being close to completion) a PhD in the area of theoretical computer science, discrete mathematics or a related discipline. A strong background in algorithms, complexity theory, and combinatorics is a plus.

This is a full time research position, no teaching is required. Starting salary is on grade 57 (code 1109/pay framework 24.1) in the Civil Service pay grade table which currently means roughly NOK 450K NOK gross
p.a., taxes should be at most 40%.

Preferred starting date: August-September 2017. The application deadline is June 01, 2017.

If you are applying, you are encourage to contact Fedor Fomin ( http://www.ii.uib.no/~fomin/ - fomin@ii.uib.no ) for more details and information.

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The research pursued by the PhD student will concern the development of new models and original solution methods for the optimization of planning operations for end-of-life products. It aims at addressing problems dealing with the newly introduced concept of circular economy and greenhouse gas emissions, recently raised by the French government. Companies are currently facing two antagonist objectives. On the one hand, they must ensure a local economic development that creates wealth; on the other hand, they must satisfy environmental constraints related to their production. In this challenging context, one of their major issues is to minimize the consumption and waste of raw materials while limiting the use of non-renewable energies.

Meanwhile, new communicating technologies could provide necessary traceability information about end-of-life products, their state and the state of their components. This information can help organizing and optimizing remanufacturing and disassembly processes. The process of valuing end-of-life products could be economically viable, and therefore valuable from an industrial point of view.

In the light of the above background, this thesis focuses on the use and advantage taking of new communicating technologies in remanufacturing and disassembly systems. The goal is to develop new models and original solution methods for planning remanufacturing and disassembly systems that exploit this new procured information, while satisfying both resource and environmental constraints. The proposed news approaches will be defined and tested with industrial partners.

Candidate profile: Applicants must have a Master Degree (or equivalent) in Computer Sciences, Applied Mathematics or any related discipline. Applicants should demonstrate good programming skills and a deep knowledge in combinatorial optimization and integer programming.

Location: The PhD student will be located in Gardanne (south of France, near Aix-en-Provence) in the research group Manufacturing Sciences and Logistics of the Georges Charpak Provence Campus of the Ecole des Mines de Saint-Etienne.

Duration and assumption of duty: The position is for three years starting September 1, 2017 (exact date is negotiable).

Application procedure: Please send your application electronically (preferably as a single pdf file) including a detailed curriculum vitae and examination results, plus, if available, a list of reference letters and copies of diploma, to: Nabil ABSI (absi@emse.fr) or Valeria BORODIN, (valeria.borodin@emse.fr). The position will remain open until fulfilled.

For further questions regarding the position or details on the research project, please contact Nabil ABSI or Valeria BORODIN.

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The Gran Sasso Science Institute (GSSI - http://www.gssi.it/ ), a recently established international PhD school and a Centre for advanced studies in L'Aquila (ITALY), offers 10 PhD positions in Computer Science (CS).

The PhD program in CS is mainly concerned with heterogeneous distributed systems and their interactions. Different perspectives are offered to provide students with the necessary tools for the design, the implementation, the management and the use of distributed systems. The main research areas of interest are:
- Efficient algorithms for communication networks and social networks;
- Formal methods for systems correctness and analysis;
- Software engineering for efficient and resilient applications.

Apart from pursuing their own research studies, the successful candidates will have the opportunity to cooperate with members of the research group and of the Scientific Board, as well as with the frequent guests of the Institute. Detailed information about the CS research group and about the activities for the PhD program in CS can be found at http://cs.gssi.it/

The fellowships are awarded for three years and their yearly amount is € 16.159,91 gross. Moreover all PhD students:
- will have free accommodation at the GSSI facilities and luncheon vouchers;
- will have tuition fees waived;
- will be covered by insurance against accident and/or injury.

The application must be submitted through the online form available at http://www.gssi.it/phd/ and must be accompanied by the curriculum vitae and by a letter of motivation describing expertise and general research interests together with future plans and reasons for having chosen GSSI for PhD studies.

The deadline for application is: 31st May 2017 at 18.00 (Italian time zone).

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The Faculty of Mathematics and Computer Science at the University of
Würzburg invites applications for the position of a

Leader of a Junior Research Group

starting October 1, 2017 (or as soon as possible thereafter) for a
maximum of 5 years. The research group consists of the leader, a
postdoc, and a PhD position (50%). Payment will be according to scale
E14 of the Collective Agreement for the Public Service of German Federal
States (TV-L). We are looking for an excellent young researcher from
one of the following two areas:

Discrete Optimization Strategies for Applications

or

Computational Sciences --
Algorithms for Applications in the Natural and Life Sciences
(efficient implementation of mathematical models from the
natural and life sciences on high-performance computers).

The research group is expected to strengthen the links between the
Institute of Mathematics and the Institute of Computer Science.
Experience with interdisciplinary research cooperations (e.g., in
biology, medicine, or physics) is a plus. The position comes with mild
teaching duties (5 contact hours per week) within the B.Sc. and M.Sc.
programs of the Faculty. The group leader is expected to hold a PhD (or
an equivalent degree) with very good grades, a strong publication
record, and experience in an international research environment.

Würzburg is a beautiful town in the wine region of Lower Franconia,
just 90 train minutes from Frankfurt airport. More than a quarter
of the 120,000 inhabitants of Würzburg are students. The University
and the Faculty of Mathematics and Computer Science have been growing
dynamically over the last few years, partially thanks to the Bavarian
digitalization initiative.

The junior research group is part of the "Excellent Ideas" program of
the University of Würzburg:
https://www.uni-wuerzburg.de/en/ueber/research/junior_research_groups/

The University of Würzburg is an equal opportunity employer. As such,
we explicitly encourage applications from women. Applications from
disabled persons with essentially the same qualifications will be given
preference.

Please submit your application by _May 31, 2017_ with the usual
documents (CV, certificates, list of publications, list of modules
taught) and a short research proposal (including state of the
art, potential research links to the Faculty and the University,
research plan, milestones) as pdf files via

https://go.uniwue.de/researchgroup-mi

Further information will be requested in the submission process.
Don't hesitate to contact the faculty manager:

Dr. Richard Greiner
email: greiner "at" mathematik.uni-wuerzburg.de
phone: +49 931 3185029

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Tenure-track Assistant Professor in Algorithms and Optimisation
Department of Computer Science
University of Copenhagen

The position is open from 1 November 2017 or as soon as possible thereafter.

The Department of Computer Science (DIKU) is expanding its research and
educational activities and hereby invites applications for a *tenure-track
assistant professor in Algorithms and Optimisation*. The tenure-track
assistant professor will join the department’s Algorithms and Programming
Languages Section (http://www.diku.dk/english/research/apl-group/). The
section’s research is concerned with a wide range of areas related to
algorithms, programming languages and systems. In the area of algorithms,
our research covers discrete optimization algorithms and theoretical
aspects of algorithms and data structures.

The candidate must have a strong background in at least one of the
following two sub-fields:

- Applications of discrete (optimization) algorithms and data structures
to problems in for example bioinformatics, network design, logistics.
Candidates with publications at top bioinformatics, operations research
conferences and journals will be preferred.
- Theoretical aspects of algorithms and data structures. Candidates with
publications at top algorithms/theory conferences such as STOC, FOCS, SODA,
ICALP will be preferred.Read more about the Department at
http://www.science.ku.dk/english/about-the-faculty/organisation/.

Please find the full job advertisement at http://employment.ku.dk/.

Only electronic applications are accepted.