The Uncertainty in Artificial Intelligence (UAI - http://uai.win.tue.nl) group is research group embedded in the Data and AI (DAI) cluster at the Eindhoven University of Technology (https://dai.win.tue.nl). In the UAI group we aim at developing foundations of AI, including robust AI by using imprecise-probabilistic (IP) models. To get an idea of what this means, you may think of replacing one probability model with a set of probability models. In this PhD research, we will work with an uncertainty model called 'choice functions', which is a more expressive and arguably more exciting model, but still less tractable from a computational point of view.

In the UAI group, we develop tools and algorithms for efficient calculations and inference in IP contexts. Our group consists of a unique mix of experts and is therefore very well placed to perform such research. Members of our group, and therefore also you, provide state-of-the-art research in the foundations of robust AI.

As a researcher in our group, you will first familiarise yourself with the topic of imprecise probabilities. We will help you with this, and you may do this by working on some easier, but nonetheless interesting, IP models and networks. After this, we will work on the missing links that will make the theory of choice functions be applicable in general AI tools. Some of these missing links are:

• Studying concepts of independence using choice functions;
• Finding the joint choice function given a Bayesian network with local choice functions;
• Studying assumptions that make efficient inferences possible;
• Designing algorithms for inferences with this joint model (this will involve optimalisation).

To give a gist of the type of research will be done, please have a look at the following papers, without trying to understand all the results in the papers:

• The paper that is closest to the starting point of this research, can be found at https://arthurvancamp.github.io/journal/ijar2023a/AVC-IJAR2023a-paper.pdf.
• The paper that perhaps best introduces the model we are working with, can be found at https://arxiv.org/abs/1903.00336.

As you see, there are many directions we might follow, all of which will be heavy on the mathematics side. The opportunities for fundamental research that will lead to applications are ample!

The candidate should

• Have a master's degree in Computer Science, Mathematics, or a related field;
• Have excellent analytical skills and interests;
• Have excellent academic writing and presentation skills;
• Be proficient in English, both written and spoken;
• Desire to conduct excellent research and publish in high quality conferences and journals;
• Be an independent thinker, and be self-responsible;
• Have the ability and desire to collaborate and work in teams;
• Have the ability and desire to support teaching and to co-supervise bachelor and master students.
• (Having excellent coding skills (e.g. Python, PyTorch, Tensorflow) is desirable but not required.)

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for five years, with an intermediate evaluation (go/no-go) after nine months. You will spend 25% of your employment on teaching tasks.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. €2,770 max. €3,539).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.