Are you interested in applying state-of-the-art causal methods to support patient-centric healthcare? Do you want to work in an interdisciplinary environment collaborating with machine learning experts, cognitive scientists and medical specialists? If so, then this vacancy may be for you!
Within our Personalised Care in Oncology (PersOn) consortium, we are looking for a talented PhD candidate to work on applying principled causal model inference to help patients make informed decisions about their preferred treatment alternatives. We will use modern explainability techniques, such as Causal Shapley values, to translate complex counterfactual predictions into patient-friendly, understandable information, allowing patients to explore the impact of different available treatments.
Together with other PhD candidates and postdoctoral researchers in the consortium, you will help build an integrated causal model of the target oncological domain, and use that as a basis for answering interactive `what if' questions tailored to characteristics and preferences of individual patients. You will work with causal model experts, as well as with researchers, healthcare practitioners, and stakeholders in explainable decision support. Your contribution to the project will be both foundational, advancing the state of the art in personalised causal inference, and applied, focusing on user requirements in terms of explanation and justification to help advance shared decision making about personalised healthcare.
Given the interdisciplinary challenges associated with the project, you will participate in two research groups. Your home base will be the Data Science group in the Faculty of Science at Radboud University, focusing on causal modelling and individualised counterfactual inference. In addition, for one day a week, you will be part of the Microbiology and Systems Biology research group at TNO (see below), where part of the work focuses on explainable models that bring together knowledge and data for improvement of personal health. You will also collaborate closely with PhDs from other work packages in the PersOn project, responsible for the collection/elicitation of patient preferences and modelling specific medical knowledge use cases (see related vacancy 'Designing patient-in-the-loop personalization of cancer treatment with AI' on the project website).
We will expect you to support both our groups' research activities, and to help maintain a safe, inclusive and inspiring research environment for all our team members. If so desired, you will have the ability to develop your teaching skills (ca 10 %), helping yourself further qualify for a career as an independent academic researcher.
PhD Candidate: Interactive Causal Explanations for Patient-centric Personalised Health
- 1.0 FTE
- Gross monthly salary
- € 2,770 - € 3,539
- Required background
- Research University Degree
- Organizational unit
- Faculty of Science
- Application deadline
- You have a MSc degree in natural science, computer science, mathematics, or a related discipline.
- You have a strong interest in multidisciplinary research, especially on the interface between artificial intelligence and health.
- You are highly motivated, open-minded, and determined to obtain a PhD degree.
- You are fluent in written and spoken English.
- You need to be flexible and communicative as you will be working in two different research groups.
We areYou will be embedded in the Data Science group in the Faculty of Science at Radboud University. The group's main research foci are 1) the design and understanding of deep/causal machine learning methods, 2) modern information retrieval and big data, and 3) computational immunology, with a keen eye on applications in other scientific domains, in particular healthcare, as well as industry. The Data Science group is part of the vibrant and growing Institute for Computing and Information Sciences (iCIS), consistently ranked as one of the top Computer Science departments in the Netherlands. Our group currently consists of 50 researchers, including 25 PhDs, and offers a very open, inclusive and supportive work environment.
In addition, you will work in the Microbiology and Systems Biology research group of the Health, Living and Work unit of the Netherlands Organisation for Applied Scientific Research (TNO), in close collaboration with the data science group of the ICT, Strategy & Policy unit that is working on solutions that enrich information systems and artificial intelligence (AI) with human knowledge and experience. The focus of this group is to optimise health and cure lifestyle-related disease from a systems biology view. The group is a very multidisciplinary team, including biologists, data scientists, bioinformaticians, etc.
Radboud University and TNO are equal opportunity employers, committed to building a culturally diverse intellectual community, and as such encourage applications from women and minorities. Radboud University offers customised facilities to better align work and private life, and highly values the career development of its staff, which is facilitated by a variety of programmes.
Radboud UniversityWe are keen to meet critical thinkers who want to look closer at what really matters. People who, from their expertise, wish to contribute to a healthy, free world with equal opportunities for all. This ambition unites more than 24,000 students and 5,600 employees at Radboud University and requires even more talent, collaboration and lifelong learning. You have a part to play!
- It concerns an employment for 1.0 FTE.
- The gross starting salary amounts to €2,770 per month based on a 38-hour working week, and will increase to €3,539 in the fourth year (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).
- 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 30 or 41 days of annual leave instead of the legally allotted 20.
Additional employment conditionsWork 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 Tom Claassen, Assistant Professor Data Science at +31 24 365 20 19 or Tom.Claassen [at] ru.nl (tomc[at]cs[dot]ru[dot]nl). Alternatively, you can contact Jildau Bouwman, Senior scientist TNO at +31 88 866 16 78 or jildau.bouwman [at] tno.nl (jildau[dot]bouwman[at]tno[dot]nl).
Practical information and applyingYou can apply until 6 December 2023, exclusively using the button below. Kindly address your application to Tom Claassen. Please fill in the application form and attach the following documents:
- A letter of motivation.
- Your CV.
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.
Apply now Application deadline
We would like to recruit our new colleague ourselves. Acquisition in response to this vacancy will not be appreciated.