Postdoc Position: Computational Methods for Neurodegeneration Research

Postdoc Position: Computational Methods for Neurodegeneration Research

VIB

Leuven, Belgium

About the project

We have generated advanced brain on chip models that mimick human striatal function and we are using these systems to study the mechanisms of Parkinson’s disease. The system, that involves a high density multielectrode array enables us to measure single neuron electrophysiology as well as complex network dynamics in the context of specific perturbations. You will use these data to map the disease in terms of electrophysiological dynamics and stratify disease into specific subclasses. You will develop AI algorithms to train models that predict if individuals (from which we create circuits) are prone to develop disease and to identify conditions that have high potential to suppress disease expression and/or progression.

Your job

You will work with a team of scientists interested in (1) mechanisms of neurodegeneration, (2) cellular determinants of disease, (3) the development of translational strategies in circuit neuroscience and (4) the development of computational models and methods for neuroscience. The goal is to implement AI-based methods and analysis pipelines in a unique human brain circuit model to generate mechanistic insights, with the ultimate goal of combating Parkinson’s disease. You will have access to excellent support facilities, including facilities at VIB, KU Leuven, the Flemish supercomputer and many more.

Profile

We are seeking a highly motivated, enthusiastic, critical and creative individual to join our teams. The candidate should have a strong background in a quantitative discipline and an interest in neurodegenerative diseases.

Essential:

• You obtained a PhD in Computational Neuroscience, Bioinformatics, Computer Science, Machine Learning, Physics, Mathematics, Engineering, or equivalent;
• A solid publication record with research publication(s) in peer-reviewed international journals;
• Good programming skills (ideally in Python);
• Team player and great collaborator;
• Strong interest in interdisciplinary work at the interface between dementia/ neurodegeneration, modeling, and machine learning;
• Prior experience in deep learning, or/and the statistical analysis of neural data.

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