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between genetic risk factors and neurodevelopment. This interdisciplinary, NWO-funded project brings together large-scale datasets and advanced analytical methods, including machine learning, genomics
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research will span a broad range of modelstopics, including deep learning, causal machine learning, distributed learning, unsupervised learning, foundation models, natural language processing, and
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combine these data using statistical and machine-learning approaches. Established markers such as neurofilament light chain (NfL) and GFAP will provide a biological reference point for identifying
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computational modeling; experience with neural networks for image analysis, generative modeling, geometric deep learning or physics-informed machine learning, or you are willing to learn these quickly
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modulators. Experience with programming and data analysis, for example in Python, is highly desirable. Familiarity with molecular modelling, docking workflows, machine-learning approaches or high
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imaging analysis pipelines using innovative or novel approaches. Experience with coding, specific imaging software such as GA3 Elements, machine learning or quantitative image analysis is advantageous. A