Are you interested in designing and analysing algorithms based on completely novel computing principles? And in collaborating with physics researchers to finetune Markov Chain Monte Carlo algorithms using new materials currently only available in specialist labs? Or in working towards computation principles of the next decade? Then this PhD position may be of interest to you!

In this PhD position you will develop and analyse Markov Chain Monte Carlo algorithms (such as Gibbs Sampling and Metropolis-Hastings) that make use of a novel technology that is currently only available in specialist labs. Using dedicated photonic manipulation, researchers can build covarying bits out of magnetic fields at the nanoscale that can be used at speeds that are orders of magnitude faster than current CMOS technology. In order to demonstrate the computational power of so-called synchronised stochasticity for approximate Bayesian inference, you will develop novel MCMC algorithms that make use of this feature for energy, time and space efficiency. In addition to algorithm development, implementation (using an emulator) and simulation, you will also work on analysing the theoretical properties of these algorithms using the mathematical framework of randomised complexity theory.

You will be part of both the Probabilistic Graphical Models group at the Donders Centre for Cognition and the interdisciplinary team of researchers and PhD candidates on this consortium project. In addition to research in this domain, you will contribute to academic teaching in the BSc and MSc programmes in AI, follow courses in the Donders Graduate school as well as in the national research schools IPA and SIKS, and collaborate with inspiring colleagues in the international PGM research community.

• You have an MSc in computer science, applied mathematics, artificial intelligence, or a related discipline.
• You have a strong interest in performing foundational algorithmics and computational complexity research.
• You have mature mathematical knowledge and programming skills, for example demonstrated in your MSc thesis research project.
• You are familiar with Linux and preferably have experience with programming in low-level languages such as C/C++ and assembly language and/or low-level hardware design such as VHDL.
• You have good writing and presentation skills, a good command of the English language, and demonstratble skills in interdisciplinary collaboration (e.g. in your study programme).
• Knowledge of / experience in Bayesian networks and approximate Bayesian inference is appreciated.

Fixed-term contract: 4 or 5 years.

• We will give you a temporary employment contract (0.8 FTE 5- year contract - 1.0 FTE 4- year contract) of 1,5 years, after which your performance will be evaluated. If the evaluation is positive, your contract will be extended by 2.5 years (4-year contract) or 3.5 years (5-year contract).
• You will receive a starting salary of €2,770 gross per month based on a 38-hour working week, which will increase to €3,539 from the fourth year onwards (salary scale P).
• You will receive an 8% holiday allowance and an 8,3% end-of-year bonus.
• You will be able to use our Dual Career and Family Support Service. The Dual Career Programme assists your partner via support, tools, and resources to improve their chances of independently finding employment in the Netherlands. Our Family Support Service helps you and your partner feel welcome and at home by providing customised assistance in navigating local facilities, schools, and amenities. Also take a look at our support for international staff page to discover all our services for international employees.
• You will receive extra days off. With full-time employment, you can choose between 30 or 41 days of annual leave instead of the statutory 20.

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.

You will join the Probabilistic Graphical Models Group, led by Prof. Johan Kwisthout, who will be your supervisor. In this group we study the foundations and applications of Probabilistic Graphical Models (PGMs). The group's research is centred around two research lines: ‘PGMs for decision support systems’ and ‘Foundations of stochastic computing’. The first line focuses on explainability, trustworthiness, maintainability, online or federated learning etc. in Bayesian networks and other PGM models, particularly with applications in clinical decision support systems. The second research line focuses on topics such as approximate Bayesian inference, parameterised complexity classes for stochastic computing, and realisation of probability distributions and computations on them in novel materials and computing architectures. This position contributes to the second research line.