Would you like to conduct cutting-edge research on approximate Bayesian inference and computational complexity theory? Then join the collaborative and supportive work environment of the AI section in the Donders Institute as a PhD candidate. You will be able to put your ideas to the test and push your boundaries. You will do this in a collaborative, multidisciplinary and supportive work environment, with a diverse international staff. 
Bayesian inference (the computation of a posterior probability given a prior probability and new evidence) is one of the most crucial computational techniques in artificial intelligence. However, Bayesian inference is an intractable (NP-hard) problem even when only an approximate solution is sought, implying that well-known approximation techniques for Bayesian inference, including variational Bayes, Metropolis-Hasting sampling, and likelihood weighting, only work well on a subset of problem instances, and cannot give a guaranteed quality of approximation in general. This limits the applicability of Bayesian networks for real world applications.

In this PhD project we investigate a new approach towards approximate Bayesian inference, i.e. we translate an inference problem to a weighted satisfiability instance, apply approximation strategies to give an approximate count of the (weighted) number of models of the instance, and then translate the solution back to the inference problem. This approach may open up new avenues as it allows for a new class of approximation strategies based on hashing rather than sampling or model simplification to approximately count models. Currently, however, the state-of-the-art techniques are not yet well suited for the instances that arise from the translation from a Bayesian network to a weighted satisfiability problem. In this project we study how this translation can be adjusted such that hashing approaches work, and study both experimentally and by formal parameterised complexity analysis whether this allows for a novel sub-set of Bayesian inference problems that can be tractably approximated.

In addition to research in this domain, you will contribute to teaching in the BSc and MSc programmes in AI, attend courses offered by the Donders Graduate School and the national research schools IPA and SIKS, and collaborate with inspiring colleagues in the international PGM research community. The teaching contribution for PhD candidates is 10% of your work load, i.e., 0.1 fte in case of a full time contract.

• You have an MSc in computer science, applied mathematics, artificial intelligence, or a related discipline.
• You have mature mathematical knowledge and skills, demonstrated in, for example, your MSc thesis research project.
• You have a strong interest in performing foundational algorithmics and computational complexity research.
• Knowledge of / experience with Bayesian networks, SAT solvers, or parameterised complexity theory is appreciated.

Fixed-term contract: You will be employed for an initial period of 18 months, after which your performance will be evaluated. If the evaluation is positive, the contract will be extended by 2.5 years (4 year contract) or 3.5 years (5 year contract).

• It concerns an employment for 0.8 (5 year contract) – 1.0 FTE (4 year contract).
• The gross starting salary amounts to €2,541 per month based on a 38-hour working week, and will increase to €3,247 from the fourth year onwards (salary scale P). 
• You will receive 8% holiday allowance and 8.3% end-of-year bonus.
• You will be employed for an initial period of 18 months, after which your performance will be evaluated. If the evaluation is positive, the contract will be extended by 2.5 years (4 year contract) or 3.5 years (5 year contract).
• You will be able to use our Dual Career and Family Care Services. Our Dual Career and Family Care Officer can assist you with family-related support, help your partner or spouse prepare for the local labour market, provide customized support in their search for employment and help your family settle in Nijmegen.
• Working for us means getting extra days off. In case of full-time employment, you can choose between 29 or 41 days of annual leave instead of the legally allotted 20.
• You will be part of the Donders Graduate School for Cognitive Neuroscience.

Work and science require good employment practices. This is reflected in Radboud University's primary and secondary employment conditions. You can make arrangements for the best possible work-life balance with flexible working hours, various leave arrangements and working from home. You are also able to compose part of your employment conditions yourself, for example, exchange income for extra leave days and receive a reimbursement for your sports subscription. And of course, we offer a good pension plan. You are given plenty of room and responsibility to develop your talents and realise your ambitions. Therefore, we provide various training and development schemes.

The Donders Institute for Brain, Cognition and Behaviour is a world-class interfaculty research centre that houses more than 700 researchers devoted to understanding the mechanistic underpinnings of the human mind. Research at the Donders Institute is focused around four themes: 1. Language and communication, 2. Perception, action and control, 3. Plasticity and memory, 4. Neural computation and neurotechnology. Excellent, state-of-the-art research facilities are available for the broad range of neuroscience research that is being conducted at the Donders Institute. The Donders Institute has been assessed by an international evaluation committee as ‘excellent’ and recognised as a ‘very stimulating environment for top researchers, as well as for young talent’. The Donders Institute fosters a collaborative, multidisciplinary, supportive research environment with a diverse international staff. English is the lingua franca at the Institute.

As a PhD candidate you will join the Foundations of Natural and Stochastic Computing (“Foundations”) Group, led by PI Johan Kwisthout, who will be your daily supervisor. In this group we study the foundations of natural and stochastic computing. The group's research is centred on two research lines: (I) foundations and applications of probabilistic graphical models and (II) foundations of neuromorphic computing. In the first line we study topics such as computational complexity, approximate inference algorithms, Bayesian statistics, non-parametric Bayes, explainable AI (in particular in the healthcare domain), and computational modelling of cognition, focused on the Predictive Processing account in neuroscience. In the second line we study topics such as computational complexity, neuromorphic/hybrid algorithm design (in particular with respect to Green ICT), hardware-software co-design in neuromorphic engineering, brain-inspired models of computation, and philosophy of neuromorphic computation. Your research will be embedded in the first research line.

Radboud University
We want to get the best out of science, others and ourselves. Why? Because this is what the world around us desperately needs. Leading research and education make an indispensable contribution to a healthy, free world with equal opportunities for all. This is what unites the more than 24,000 students and 5,600 employees at Radboud University. And this requires even more talent, collaboration and lifelong learning. You have a part to play!