Postdoctoral Researcher for Machine Learning for Personalised Effect Estimation of Multi-domain...

We are looking for a Postdoctoral Researcher with an interest in developing, studying and applying machine learning methods for personalised effect estimation, using data from both a large interventional study and observational data on cognitive decline.

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.