PhD-student: Efficient robust inference
Devise algorithms and design uncertainty models that make it possible to efficiently draw robust conclusions and make safe decisions in practical applications. Apply state-of-the art
learning & optimization techniques and create software implementations ready for public use.
Mathematics & Computer Science
Computer Science ,
Industrial and Applied Mathematics
Robust uncertainty models (such as imprecise probabilities) make it possible to draw conclusions (inferences) that are reliable in the face of a limited amount of data or model uncertainty. Even in a big-data world, these situations occur often. These models enable safety, trust, and reliability for applications in AI and other fields. They are more expressive, but generally also more complex than classical uncertainty models. The challenge is therefore to choose or design uncertainty models that allow for efficient inference and to actually develop the algorithms that make it possible to compute those inferences efficiently. The exact topic of the PhD research is determined together with you and can range from theoretical to applied. We encourage you to express your preferences or even formulate your own research proposal.
Some example topics:
- learning robust surrogate models of complex systems;
- hybrids of qualitative and credal networks (generalizations of Bayesian networks);
- inference algorithms for finitary robust probabilistic models (such as sets of desirable gambles).
What you will be doing
In collaboration with your supervisor and other researchers in the field, you will
- carry out research on the determined research topic;
- develop and maintain software tools that implement your research results;
- publish your results in scientific papers and elsewhere;
- present your results at international scientific meetings.
In collaboration with colleagues at TU/e, you will contribute to the group's teaching and supervision activities.
Finally, you will personally write and defend a PhD thesis that bundles your results as a coherent whole.
The Uncertainty in Artificial Intelligence (UAI) group is a new and quickly growing group embedded in the AI cluster at the Eindhoven University of Technology. This cluster aims at developing foundations of AI for the present and the future. This includes the design of new AI methods, development of AI algorithms and tools with a view at expanding the reach of AI and its generalization abilities. In particular, we study foundational issues of robustness, safety, trust, reliability, tractability, scalability, interpretability and explainability of AI.
We are looking for candidates that match the following profile well:
- passionate about mathematics with a strong affinity to computer science (so MSc in Mathematics, Computer Science, or a related field);
- inquisitive and analytical nature;
- independent, but also a good team player;
- self-critical, persevering, and with an eagerness and capacity to learn;
- a clear and effective speaker, writer, and presenter (English);
- a programmer that produces well-structured, clean, and efficient code (experience in languages and tools used in AI is a plus).
Make sure that in your application you demonstrate with concrete examples or describe how you meet these requirements. Highlight your strong points and also mention your weaker points.
We offer you:
- An exciting job in a dynamic work environment
- A full time appointment for four years at Eindhoven University of Technology (www.tue.nl/en)
- The salary is in accordance with the Collective Labour Agreement of the Dutch Universities, increasing from € 2,395 per month initially, to € 3,061 in the fourth year.
- An attractive package of fringe benefits, including end-of-year bonus (8,3% in December), an extra holiday allowance (8% in May), moving expenses and excellent sports facilities.
Informatie en sollicitatie
If you are interested in this PhD student position, use the ‘apply now’ button.
In your application, in English, you must include:
- a structured motivation letter including your research experience, research interests, and a short outline of the preferred research topic (up to two pages and less than 7000 characters);
- a CV including education history, relevant courses and theses, and, if available, previous research experience, teaching experience, or other relevant professional experience (up to two pages);
- a list including at least your MSc thesis (or theses), but also any abstracts, papers, presentation slides, recorded presentations, public code, etc. ordered by relevance (we will likely not look beyond the first three entries, apart from the MSc thesis), each accompanied by a link allowing us to access the material;
- contact details of or recommendation letters (with contact details) from 2 references.
For further information concerning the position, please contact. dr. ir. Erik Quaeghebeur
For information concerning employment conditions you can contact:
HR Services Mathematics and Computer Science (HRServices.MCS[at]tue.nl)
More information on employment conditions can also be found here: