Postdoc Researcher for ML for Personalised Effect Estimation of Multi-domain Lifestyle Interventions in Cognitive Health
|Published||November 19, 2022|
Are you a Postdoc with an interest in developing, studying, and applying machine learning methods for personalized effect estimation? Do you want to use data from both a large interventional study and observational data on the cognitive decline? If so, you have a part to play! We are looking for a Postdoc who wants to work with our leading experts on machine learning.
The Institute for Computing and Information Sciences (iCIS) at Radboud University is looking for a Postdoctoral Researcher for the NWO crossover research programme project `Maintaining Optimal Cognitive function In Ageing' (MOCIA). The aim of MOCIA is being able to signal an increased risk of cognitive decline and improve prevention by developing a personalised lifestyle intervention. The data science section at iCIS is involved as leader of work package 2 (WP2) `Non-invasive markers for cognitive decline and intervention response'.
The research focus of this postdoctoral position is the development and application of machine learning and statistical techniques for identifying factors that influence the effect of a multi-domain lifestyle intervention on cognitive health. This type of research involves the identification and formalisation of fundamental problems related to the type of data and tasks analysed within MOCIA, and the development and investigation of new causal inference methods tailored to the identified problems.
You will be appointed at the Data Science Section of the Radboud institute for Computing and Information Sciences (iCIS), where your colleagues will be leading experts on machine learning. You will have the opportunity to closely collaborate with experts on machine learning and causality (e.g. Jesse Krijthe, Marco Loog and Elena Marchiori), the two MOCIA PhD candidates appointed at iCIS (Wieske de Swart and Wouter Kant), and other members of the MOCIA consortium.
- It concerns an employment for 1.0 FTE.
- The gross monthly salary amounts to a minimum of €2,960 and a maximum of €4,670 based on a 38-hour working week, depending on previous education and number of years of relevant work experience (salary scale 10).
- You will receive 8% holiday allowance and 8.3% end-of-year bonus.
- It concerns a temporary employment for 2 years, with the option to extend for a further 2 years after a positive evaluation.
- 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.
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.
You can apply until 8 January 2023, exclusively using the button below. Kindly address your application to Elena Marchiori. Please fill in the application form and attach the following documents:
- A letter of motivation.
- Your CV.
- If applicable, a link to the PDF of a publication you are most proud of, or deems relevant to the position.
The first round of interviews will take place on Monday 16 January.
You would preferably begin employment on 15 February 2023.
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.
For questions about the position, please contact Elena Marchiori, Full Professor at email@example.com. Alternatively, you can contact Jesse Krijthe, Assistant Professor at Jesse.Krijthe@ru.nl.
- You should hold a PhD in Computer Science, Artificial Intelligence, Biostatistics, Epidemiology or a related discipline.
- You have an interest in conducting research on causal inference.
- You have extensive experience with machine learning.
- You have very good programming skills in Python, R or a similar computer language.
- You have a very good command of spoken and written English.
- You have the desire to bridge the gap between theory and applications.