Vacancy@UM: PhD Candidate in Algorithmic Game Theory under Uncertainty
Mark Winands
PhD Candidate in Algorithmic Game Theory under Uncertainty
Are you excited about how mathematics can be used to describe real-life scenarios? Are you curious to see how game theory plays a role in this? Then join us!
Job description
Many
real-life problems can be described by a collection of interacting agents
making decisions in an uncertain environment. As an example, consider the
energy grid, where companies and prosumers (who produce and consume energy at
the same time) try to handle the uncertainty coming from renewable sources and
demand imbalances. How can the agents take into account this unpredictability
to optimize their decision-making process?
The most suitable tool to describe these systems is game theory. A game allows
us to model a collection of agents whose “happiness” depends also on the
decisions of the other participants. In particular, stochastic Nash equilibrium
problems allow to describe the behavior of the agents in presence of
uncertainty and to characterize it. One of the main objectives is then to find
distributed optimization algorithms which are guaranteed to converge to an
equilibrium (i.e., the optimum of each agent, given the actions of the other
participants) despite these uncertainties. The goal of this project is to
design such algorithms for stochastic Nash equilibrium problems. To achieve
this goal, you will use tools from monotone operator theory, stochastic
(non-convex) optimization and stochastic programming. The outcome of the
proposal will be a characterization of how uncertainty plays a role in Nash
equilibrium problems, focusing in particular on distributionally robust Nash
equilibrium problems and chance-constraints. Moreover, distributed iterative
schemes will be developed with provable convergence guarantees toward the
equilibrium.
We are looking for a motivated and talented PhD student with a background in
the area of Game Theory and Optimization. The successful candidate will be
responsible for developing state-of-the-art algorithms that can take into
account uncertainties and provide convergence guarantees towards the Nash
equilibrium. Your research task will be to propose and validate such algorithms
and find suitable application domains for these models. Your work will advance
the theoretical analysis as well as potentially have a societal impact via its
possible applications.
Your responsibilities will include:
- To perform scientific research in algorithmic game theory and its applications.
- To be able to work independently, as well as collaboratively in both research oriented and in applied settings.
- To publish the main results in top-tier (international) conferences and in international journals.
- To assist with educational tasks, e.g., in tutorial sessions and in supervising students and internships.
Requirements
- M.Sc. degree (completed, or to be completed shortly) in (Applied) Mathematics, Econometrics, Operations Research, Computing Sciences, Artificial Intelligence or closely related fields. Candidates with a strong academic background and industry experience will also be considered.
- Demonstrated (e.g., through publications, code, projects, etc.) interest and experience with game theory and/or optimization.
- Proficiency in English (oral and written).
- Excellent communication skills.
Additionally, we would like it if you have:
- Experience with programming (Matlab)
- Experience with machine learning.
Conditions of employment
What
we offer
As Phd Candidate
at Faculty of Science & Engineering, you will be employed by the most
international university in the Netherlands, located in the beautiful city of
Maastricht. In addition, we offer you:
- Good employment conditions. The position is graded in scale P according to UFO profile PhD Candidate, with corresponding salary based on experience ranging from €2770,00 and €3539,00 gross per month (based on a full-time employment of 38 hours per week). In addition to the monthly salary, an 8.0% holiday allowance and an 8.3% year-end bonus apply.
- An employment contract for a period of 12 months with a scope of 1,0 FTE. Upon a positive evaluation, an extension of 3 years will follow.
- At Maastricht University, the well-being of our employees is of utmost importance, we offer flexible working hours and the possibility to work partly from home if the nature of your position allows it. You will receive a monthly commuting and internet allowance for this. If you work full-time, you will be entitled to 29 vacation days and 4 additional public holidays per year, namely carnival Monday, carnival Tuesday, Good Friday, and Liberation Day. If you choose to accumulate compensation hours, an additional 12 days will be added. Furthermore, you can personalize your employment conditions through a collective labor agreement (CAO) choice model.
- As Maastricht University, we offer various other excellent secondary employment conditions. These include a good pension scheme with the ABP and the opportunity for UM employees to participate in company fitness and make use of the extensive sports facilities that we also offer to our students.
- Last but certainly not least, we provide the space and facilities for your personal and professional development. We facilitate this by offering a wide range of training programs and supporting various well-established initiatives such as 'acknowledge and appreciate'.
The terms of employment at Maastricht University are largely set out in the collective labor agreement of Dutch Universities. In addition, local provisions specific to UM apply.
Employer
Maastricht University
Why
work at Maastricht University?
At Maastricht University (UM), everything revolves around the future. The
future of our students, as we work to equip them with a solid, broad-based
foundation for the rest of their lives. And the future of society, as we seek
solutions through our research to issues from all around the world. Our six
faculties combined provide a comprehensive package of study programmes and
research.
In our teaching, we use the Problem-Based Learning (PBL) method. Students work
in small groups, looking for solutions to problems themselves. By discussing
issues and working together to draw conclusions, formulate answers and present
them to their peers, students develop essential skills for their future
careers.
With over 22,300 students and more than 5,000 employees from all over the
world, UM is home to a vibrant and inspiring international community.
Are you drawn to an international setting focused on education, science and
scholarship? Are you keen to contribute however your skills and qualities
allow? Our door is open to you! As a young European university, we value your
talent and look forward to creating the future together.
Department of Advanced Computing Sciences
The Department of Advanced Computing Sciences is Maastricht University’s
largest and oldest department broadly covering the fields of artificial
intelligence, data science, computer science, mathematics and robotics.
Over 100 researchers work and study in the Department of Advanced Computing
Sciences, whose roots trace back to 1987. The department’s staff teaches more than
1,200 bachelor’s and master’s students in 4 specialized study programmes in
Data Science, Artificial Intelligence and Computer Science.
Additional information
Curious?
Are you interested in this exciting position but still have questions? Feel
free to contact Barbara Franci, Assistant Professor, via b.fr...@maastrichtuniversity.nl for more
information.
Applying?
Or are you already convinced and ready to become our new PhD Candidate? Apply
now, no later than 19 May, for this position at:
To apply for the position, submit the following documents:
- cover letter (1 page max), which includes a motivation of your interest in the vacancy and an explanation of why you would fit well for the PhD position;
- a detailed curriculum vitae;
- a course list of your Masters and Bachelor programs (including grades);
- results of a recent English language test, or other evidence of your English language capabilities;
- name and contact information of two references
The vacancy is open for internal and external candidates. In case of equal
qualifications, internal candidates will be prioritized.
Maastricht University is committed to promoting and nurturing a diverse and
inclusive community. We believe that diversity in our staff and student
population contributes to the quality of research and education at UM, and
strive to enable this through inclusive policies and innovative projects led by
teams of staff and students. We encourage you to apply for this position.