PhD Candidate for Software Correctness

  • Employment: 0.8 - 1.0 FTE
  • Gross monthly salary: € 2,443 - € 3,122
  • Faculty of Science
  • Required background: Research University Degree
  • Application deadline: 6 March 2022

Software has shaped almost every aspect of our modern lives. Ensuring that software is correct, is both a major scientific challenge and an enterprise with enormous social relevance. Would you like to examine possibilities to introduce a theory for correctness levels for software? Then you have a part to play as a PhD Candidate.

The correctness of software is of major importance in computer science. Unfortunately, the significance of software correctness is not always clear. Furthermore, the automatic checking of software correctness is difficult. This leads to problems during system development projects and during the grading of software exercises. 

This PhD candidate position is intended for four years. You understand the importance of correct software and know how to work with several meanings of software correctness. Your goal is to introduce a generic and formal theory of software correctness levels, in which partially correct/incorrect software can be handled in a flexible way.

You will put the generic theory into practice, by experimenting with automatic grading of software exercises in the context of our courses. One application will be dealing with automatic grading of SQL statements. You will be supervised by Dr Patrick van Bommel and Prof. Djoerd Hiemstra.


  • You are an enthusiastic and motivated researcher.
  • You should have a Master's degree in computer science, or a Master's degree in mathematics and a demonstrable interest in computer science.

We are

The position is available in the Software Science group of the Institute for Computing and Information Sciences (iCIS) at Radboud University. Our mission is to do top research on the use of models for design and analysis of software, bridging the gap between theory and applications. We offer joining a fun and diverse group that maintains a range of international cooperations. In 2021, we published our research in top conferences such as CAV, TACAS, (formal methods) AAAI (artificial intelligence) and RSS (robotics). Furthermore, our group actively contributes to the development of the state-of-the-art probabilistic model checker Storm.

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!

We offer

  • Employment for 0.8 (5 year contract) - 1.0 (4 year contract) FTE.
  • The gross starting salary amounts to €2,443 per month based on a 38-hour working week, and will increase to €3,122 from the fourth year onwards (salary scale P).
  • You will receive 8% holiday allowance and 8.3% end-of-year bonus.
  • You will be appointed 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.
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.

Would you like more information?

For questions about the position, please contact Patrick van Bommel, Assistant Professor at +31 24 365 25 09 or

Practical information and applications

You can apply until 6 March 2022, exclusively using the button below. Kindly address your application to Patrick van Bommel. Please fill in the application form and attach the following documents:

  • A letter of motivation.
  • Your CV including the contact details of two referees.
  • A list of courses taken and grades obtained.

The first round of interviews will take place on 15 and 17 March. You would preferably begin employment at the start of the second half of 2022.

We can imagine you're curious about our application procedure. It offers a rough outline of what you can expect during the application process, how we handle your personal data and how we deal with internal and external candidates.

PhD candidate 'Machine learning for digital biomarkers of Parkinson’s disease'

  • 36 hours per week
  • Temporary, 4 years
  • Salary scale 10A
  • Fulltime: min € 2570 - max € 3271 gross per month
  • Apply before 7 March 2022
  • Date first interview: 15 March 2022

We are looking for a talented PhD candidate to join our multidisciplinary research group working on the development of novel digital biomarkers for Parkinson’s disease. You will work on developing and evaluating various machine learning models to analyze large amounts of real-life sensor data collected in multiple cohort studies. You will be part of the Center of Expertise for Parkinson & Movement Disorders (supervision of Prof. Bas Bloem) and the Department of Data Science (supervision of Prof. Tom Heskes). In addition, the project is an integral part of an international consortium, involving data scientists and clinical researchers from e.g. University of Birmingham, Oxford University and Verily Life Sciences.

The paradigm of how we quantify Parkinson’s disease is shifting from in-clinic evaluations to objective, continuous measures of disease severity obtained in daily life. The project aims to develop the tools needed to extract digital biomarkers for disease progression from wearable sensor data, collected in
the context of large, longitudinal studies such as the Personalized Parkinson Project, conducted at the Radboudumc. The project has a strong analytical focus, and you will work on developing various deep learning (e.g. recurrent neural networks, variational auto-encoders) and statistical machine learning models to capture the temporal dynamics and periodicity of Parkinson symptoms, and to perform fusion of different sensor types. You will work closely together with clinical experts to interpret the results and build longitudinal models to better understand the heterogeneity in Parkinson’s disease progression. In addition, you will work on making the results available to the community for use in future studies, for example to provide more sensitive outcomes for clinical trials aiming to halt the underlying neurodegeneration.

Your tasks and responsibilities are:

  • Design and conduct scientific research in a stimulating, interdisciplinary environment.
  • Develop various deep learning and statistical machine learning models to analyze wearable sensor data, collected in large cohort studies.
    Interpret findings in close collaboration with clinical and machine learning experts.
  • Work together with clinical researchers to use the developed tools to build longitudinal models for Parkinson’s disease progression.
  • Present results at scientific conferences and in peer-reviewed publications.
  • Develop open source tools to make the results available for the community.
  • Follow relevant courses at the Radboudumc & Radboud University.
  • Finalize the project with a PhD thesis.