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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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and Saeys teams. In this research project you will develop and apply algorithms to link clinical phenotypes of metastasis to molecular phenotypes in mouse models. It is known that metastases exhibit
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expertise to dig deep into computational modelling, while working closely together with the experimental side of the lab. This interdisciplinary atmosphere has been a main catalyst for many past successes
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diverse classes of inhibitory neurons are specified and integrated into brain circuits during development. Our work bridges developmental neurobiology, disease modeling, and systems neuroscience. To do
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. The selected candidate will be working closely with a team of “drug hunters” exploring some of the latest advances in structural modelling and artificial intelligence applied to drug discovery. She/he
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-of-the-art in vivo perturbational technologies and advanced computational modelling to solve fascinating biomedical puzzles. This interdisciplinary atmosphere has been a main catalyst for many past successes
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of novel mechanistic insights is gained through the application of novel probabilistic deep-learning models that automatically extract biological and statistical knowledge from your in vivo perturbational
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advance, a set of untargeted and targeted spatial, single-cell, single-nucleus, and perturbational omics technologies. You will use these datasets, coming from both model organisms and patients, to uncover
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central goal is to understand how these effects are shaped by human-specific SRGAP2 genes, which regulate synaptic timing via SYNGAP1. To achieve this, we will use: Xenotransplantation models of human
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or altered behavioral responses in ASD models How these changes reflect underlying shifts in cortical circuit function You will work within the Bonin and Farrow Labs, which brings deep expertise in