Key Skills

Job Description

Are you interested in advanced computational modelling and state-of-the-art scientific software? Join us in creating the Bayesian multiverse: a computational framework for robust statistical analyses, applicable across empirical research domains. The position offers the opportunity to work on the combination of state-of-the-art scientific computing and Bayesian statistics, and to contribute to an open-source scientific software library. You will be part of a larger project at the Uncertainty in Complex Systems lab (PI: Dr Max Hinne) aimed at learning which statistical and/or computational model is most suited for a provided data set and scientific question. In practice, statistical models contain many arbitrary choices that may (a) have a detrimental effect on what we can learn from it and (b) greatly reduces the reproducibility of scientific research, as different researchers make different decisions. A Bayesian multiverse is a systematic way of reducing these adverse effects and aims to make statistical analyses more robust. We are looking for a research assistant to contribute to the development of bamojax (Bayesian modelling in JAX), our open-source Python toolbox for Bayesian modelling and model comparison. Throughout your employment, you will have the opportunity to work with others within our group to see what modelling choices they make, and how our new Bayesian multiverse tools can help with this. There will be weekly meetings with the PI to discuss the project's progress and next steps, as well as bi-weekly lab meetings. You will have two core tasks. The first is to implement advanced methods for Bayesian model comparison in JAX. These techniques will then be used to systematically explore alternative models for user-defined statistical problems. The second task is to construct an efficient search procedure for variants of a given model. Profile You have an MSc degree in computer science, data science, AI, applied mathematics, or a related discipline. You have demonstrable experience with programming in Python and implementing statistical or machine learning algorithms. You have experience with software development practices such as testing and version control with Git. You have a solid mathematical background in, for example, probability, statistics, calculus and linear algebra. You have good communication skills. You have a passion for improving the scientific process. Prior experience with Bayesian statistics and/or JAX is a plus. We offer We will give you a temporary employment contract of 1 year. Your salary within salary scale 7 depends on your previous education and number of years of (relevant) work experience. The amounts in the scale are based on a 38-hour working week. You will receive an 8% holiday allowance and an 8,3% end-of-year bonus. 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. Additional Employment Conditions 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. We are 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: Language and communication. Perception, action and decision-making. Development and lifelong plasticity. Natural computing 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 and supportive research environment with a diverse international staff. English is the lingua franca at the Institute. Practical information and applying You can apply only via the button below. Address your letter of application to Max Hinne. In the application form, you will find which documents you need to include with your application. We look forward to receiving your application. The first interviews will take place on Monday 2 November. Any second interview will take place on Thursday 19 November. Prior to the second-round interview, candidates will receive an assignment, which they will be asked to present during the interview. You will preferably start your employment on 1 January 2027. We can imagine you're curious about our application procedure. It describes what you can expect during the application procedure and how we handle your personal data and internal and external candidates. If you wish to apply for a non-scientific position with a non-EU nationality, please take notice of the following information. Do you already work at Radboud University and have questions about making an internal move? Learn more about internal applications.

Core Responsibilities

Implement advanced Bayesian model comparison methods in JAX and develop an efficient procedure for searching variants of user-defined models. Contribute to the open-source bamojax Python toolbox and collaborate with the research group to apply Bayesian multiverse tools to statistical modelling choices.

Requirements

Applicants must have an MSc in computer science, data science, AI, applied mathematics, or a related discipline, along with demonstrable Python programming and experience implementing statistical or machine-learning algorithms. They should have experience with software development practices such as testing and Git, a solid mathematical background, and good communication skills; Bayesian statistics or JAX experience is a plus.

Benefits

• Holiday Allowance
• End-Of-Year Bonus
• Annual Leave
• Flexible Working Hours
• Leave Arrangements
• Work-From-Home Options
• Sports Subscription Reimbursement
• Pension Plan
• Training And Development