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  3. PhD candidate ‘Predicting resilience and recurrence in major depressive disorder using machine learning based normative models.’

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PhD candidate ‘Predicting resilience and recurrence in major depressive disorder using machine learning based normative models.’

We are searching for a talented PhD candidate to work on a translational project bridging the gap between machine learning, neuroscience and clinical …

4 maanden geleden


Geert Grooteplein Zuid, Nijmegen, Gelderland
Tijdelijk contract / Tijdelijke opdracht
Uren per week:
36 - 36 uur
€ 2422 - € 3103 per maand


We are searching for a talented PhD candidate to work on a translational project bridging the gap between machine learning, neuroscience and clinical psychiatry. The project focuses on developing advanced statistical and machine learning methods for the analysis of clinical neuroimaging data. The project will be hosted jointly between the Cognitive Neuroscience and Psychiatry departments at the Donders Institute for Brain, Cognition and Behavior at Radboud University Medical Center under the supervision of Dr. Andre Marquand and Dr. Eric Ruhé.

The successful candidate will be embedded within the Statistical Imaging Neuroscience research group, headed by Prof. Christian Beckmann and the Stress-Related Disorders research theme at Radboudumc as well as being part of a wider academic research environment at the Donders Institute.

The primary focus of this project is to develop statistical and machine learning approaches for understanding inter-individual variation in biomarkers derived from magnetic resonance imaging (MRI) data, then use these biomarkers to predict resilience and risk in recurrent major depressive disorder (rMDD). You will employ and extend the normative modelling ('brain growth charting') approaches we have pioneered for this purpose which rely on state-of-the art Bayesian machine learning and deep learning techniques. You will first develop the statistical machinery necessary to apply these methods to 'big data' neuroimaging samples derived from tens of thousands of multi-modal MRI scans (functional MRI, structural MRI and connectivity) e.g. the ABCD study, the Healthy Brain Network and the UK Biobank (N>40k).

You will then apply these models to richly phenotyped clinical samples of patients with rMDD (not depressed at the time of scanning) derived from samples acquired in the Netherlands and via our international collaborations (e.g. with Dr. Roland Zahn at King's College London). The goal is to make predictions about the future course of the disorder (e.g. which subjects will remain in remission and which subjects are likely to have a future depressive episode). The project is highly interdisciplinary, has a clear translational focus and integrates machine learning and statistics with cognitive neuroimaging and clinical neuroscience.

Tasks and responsibilities

  • Discuss, plan and perform research in a stimulating environment.
  • Develop statistical approaches for data analysis from fundamental principles.
  • Apply these statistical models to large-scale population cohorts.
  • Interpret findings in the light of clinical knowledge.
  • Publish findings in peer-reviewed journals and present at international scientific conferences.
  • Produce software tools to enable for the use of the wider scientific community.
  • Finalize PhD training and project within the four year contract .
  • Work in an interdisciplinary team of international scientists.


We are looking for highly self-motivated candidates who are curious and enthusiastic about scientific research and who will work together with others in our labs and in the institute to solve problems and contribute to high-quality neuroscience investigations.

  • Completion of undergraduate (and ideally also master's level) degree in a numerate discipline such as statistics, computer science, engineering, cognitive neuroscience / psychology or other relevant field of study.
  • Proficiency in programming in languages such as Python, MATLAB, R or C++.
  • Experience with neuroimaging data analysis techniques and software (e.g. SPM, FSL, FreeSurfer).
  • Experience with acquisition of MRI data would be a bonus.
  • A demonstrable academic track-record would be a plus (e.g. publications in international journals).
  • An interest in clinical applications of machine learning.
  • A proactive attitude, good written and oral communication skills, and be able to work effectively in an interdisciplinary team.


The position is for 48 months and should begin early in 2020.

Upon commencement of employment we require a certificate of conduct (Verklaring Omtrent het Gedrag, VOG) and there will be, depending on the type of job, a screening based on the provided cv. Radboud university medical center's HR Department will apply for this certificate on your behalf.

Read more about the Radboudumc employment conditions and what our International Office can do for you when moving to the Netherlands.

Additional information

All additional information about the vacancy can be obtained from Dr. Andre Marquand, Computational Neuroscientist or from Dr. Eric Ruhe, psychiatrist . Use the Apply button to submit your application.

Please apply before November 15.
Recruitment agencies are asked not to respond to this job posting.