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  3. PhD candidate 'Develop statistical and machine learning tools for smartphone monitoring and neuroimaging data'

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PhD candidate 'Develop statistical and machine learning tools for smartphone monitoring and neuroimaging data'

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

5 maanden geleden


Geert Grooteplein Zuid, Nijmegen, Gelderland
Tijdelijk contract / Tijdelijke opdracht
Uren per week:
36 - 36 uur
€ 2495 - € 3196 per maand


We are looking for a talented PhD candidate to work on a translational project bridging the gap between machine learning, neuroscience and clinical psychiatry. The project aims to develop and apply advanced statistical and machine learning methods for the analysis of passive smartphone monitoring ('digital phenotyping') data. In addition, you link these data to measures of neurobiology derived from brain imaging (structural MRI, functional MRI and brain connectivity data). Will you join our team?

The ultimate aim of this project is to integrate quantitative measures of biology and behaviour in order to predict illness trajectories in mood disorders (depression and bipolar disorder). This project is hosted by the Donders Institute for Brain, Cognition and Behavior at the Radboud University Medical Center (Cognitive Neuroscience department) under the supervision of Assoc. Prof. Andre Marquand and Prof. Christian Beckmann and integrated into the wider academic research environment at the Donders Institute.

This project is highly interdisciplinary, has a clear analytical focus and integrates machine learning and statistics with cognitive neuroimaging and clinical neuroscience. You will be expected to develop machine learning techniques from basic principles, including both classical statistical and machine learning techniques (e.g. Bayesian and regularization-based methods) and deep learning technology (e.g. recurrent neural networks). Furthermore, you will also be expected to produce software tools to enable other researchers to take advantage of the innovations produced in this project. In addition, the project will also involve curation and quality control of large digital phenotyping datasets plus elements of multi-modal data fusion.

An integral part of this international project is a two-year secondment to the University of Chicago in the USA to work with the developers of the BiAffect digital phenotyping platform (Assoc. Prof Alex Leow). Note that the timing of this visit can be adjusted according to the candidate selected.

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 a four-year contract.
  • Work in an interdisciplinary team of international scientists.


We are looking for a highly self-motivated PhD candidate who is curious and enthusiastic about scientific research. You will work together with others in our labs and in the institute to solve problems and contribute to high-quality neuroscience investigations. It is important that you have a strong interest in biological or theoretical neuroscience, and an interest in clinical applications of machine learning. You have a proactive attitude, good written and oral communication skills. Furthermore, you are able to work effectively in an interdisciplinary team.

In addition you possess the profile below:

  • Completion of undergraduate and master's level degrees 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++.
  • Prior hands-on experience in machine learning and AI-based techniques is vital along with demonstrable experience in programming.
  • Prior experience with similar sources of data (e.g. smartphone monitoring, wearable sensors or ecological momentary assessment) is desired, but not essential.
  • Experience with neuroimaging data analysis techniques and software (e.g. FSL, FreeSurfer, SPM) is desired, but not essential.
  • A demonstrable academic track-record would be a plus (e.g. publications in international journals).


You will be appointed for an initial period of 24 months, after which your performance will be evaluated. If the evaluation is positive, the contract will be extended by 2 years (4 year contract). Working at Radboud university medical center means that you are ahead of the curve and working together on the healthcare of the future. And there is more. Our secondary terms of employment are impressive. These are fully tailored to you thanks to our Employment Conditions Selection Model. At Radboud university medical center, you will be given trust, and you will take the responsibility to handle everything together. We provide annual courses, both professional and personal.
  • In addition to your monthly salary and an annual vacation allowance of 8%, you will receive an end-of-year bonus of 8.3%.
  • If you work irregular hours, you will receive an allowance.
  • As a full-time employee (36 hours per week), you are entitled to approximately 168 vacation hours (over 23 days) per year.
  • Radboud university medical center pays 70% of the pension premium. You pay the rest of the premium with your gross salary.
  • You get a discount on health insurance as well: you can take advantage of two group health insurance plans. UMC Zorgverzekering and CZ collectief.
In addition to our terms of employment, we also offer employees various other attractive facilities, such as childcare and sports facilities. Want to learn more? Take a look at the Cao UMC.

Additional information

All additional information about the vacancy can be obtained from dr. Andre Marquand, associate professor. Use the Apply button to submit your application.