Research assistant deep-learning applications to polygenic scores in neuroscience and mental health

We offer a position as research assistant where you will work together with researchers from our group in developing and applying deep-learning models for the construction of polygenic scores. This project involves setting up a framework for the training and validation of deep-learning models that take genotyped DNA of human subjects as input to predict phenotypic traits, such as (longitudinal changes in) brain structures, cognition, and mental health. One part of this project is incorporating additional sources of knowledge into the models, such as prior knowledge on the association of the genetic marks with the trait, structure of the human genome, functional annotations of the genetic marks, and demographic and clinical variables of the subjects. Another part will be introducing explainable AI (XAI) into the models to better understand and help to improve how the models work. This project does not involve the collection of new data from human subjects as the models will be applied to data from existing cohorts. The aim of this project is to produce a framework for constructing polygenic scores that can be used by researchers in their study, with the goal to publish the method in a scientific journal and make the source code publicly available.

We are looking for a candidate that has affinity with computational sciences. Preferably a candidate with experience in deep-learning or machine learning technologies, but any candidate with a background in bioinformatics, computer science, mathematics, or related fields are welcome to apply. We value any affinity with biology, genetics, and/or neuroscience, but this is not required. This position is also open to apply for master students who have not yet graduated. The ideal candidate would (partially) match the following skills:

• Experienced in computer programming, preferably Python.
• Experience with deep-learning or machine learning technologies, such as Keras, TensorFlow, PyTorch, or Scikit-learn, including training models on a GPU.
• Experience with responsible AI, in particular the topic of explainable AI (XAI).
• Experience with working on a high performance compute cluster and/or Unix systems.
• Affinity with genetics at the level of genotyped DNA, or at least a basic understanding of human genetics at high school level.
• Some affinity with neuroscience and/or behavioral sciences.
• Some affinity with research and/or ambition for a career in academic/scientific research.
• Proficiency in Dutch and/or English.

For questions, you can contact Hilleke Hulshoff Pol (h.e.hulshoffpol@umcutrecht.nl) and Jalmar Teeuw (j.teeuw@umcutrecht.nl).

The maximum salary for this position (32 - 32 hours) is € 3.772,00 gross per month based on full-time employment.

In addition, we offer an annual benefit of 8.3%, holiday allowance, travel expenses and career opportunities. The terms of employment are in accordance with the Cao University Medical Centers (UMC).

The position is embedded within the UMC Utrecht in the Brain Plasticity research group at the department of psychiatry. Our Brain Plasticity research group focuses on the neurobiological aspects of brain development and aging. We are particularly interested in how changes in the structure and function of the brain are related to cognition and mental health. Part of this effort is to elucidate the role of genetics on the brain and human behavior. You will work together with a small team of researchers located at UMC Utrecht and Utrecht University. Your work will be supervised by Prof. dr. Hilleke Hulshoff Pol and Dr. Jalmar Teeuw. Your office will reside at UMC Utrecht, with the option to work remotely from home. Every other week the entire Brain Plasticity research group will meet at Utrecht University (or online) to discuss updates and progress on their work. Every once in a while we go out together for lunch/dinner and fun activities.