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RNA-Seq, ChIP/DAP-Seq protein-DNA interaction data, bulk, and single-cell ATAC-Seq) and the application of diverse supervised machine learning approaches (e.g., feature-based, deep learning, and
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. NERF is a joint research initiative by imec, VIB and KU Leuven. The project We are looking for a PhD candidate interested in developing machine learning methods and applying them to neuroscience problems
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, probabilistic modelling, generative AI) or machine learning Proficient in Python or R programming Strong communication skills in English Desirable but not required Preference will be given to candidates with
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systems biology Background in AI (deep learning, probabilistic modelling, generative AI) or machine learning Proficient in Python or R programming Strong communication skills in English Desirable but not
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bioinformatics Experience with applying AI (deep learning, probabilistic modelling, generative AI) or machine learning in the field of systems biology Proficient in Python or R programming Strong communication
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-of-the-art molecular biology techniques, multimodal data generation and integration, gene regulatory network reconstruction and wide range of machine learning approaches The host labs will provide financial
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excellent academic performance, especially in relevant subjects, such as mathematics, statistics, machine learning and bioinformatics. You have strong programming skills in Python (R is a plus) You have
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are currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning. In particular, we develop machine learning methods to derive
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Description The Yves Moreau and Stein Aerts research labs are looking for a shared PhD student to apply deep representation learning and large language models to decipher the genomic regulatory code
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-existing cell-based assays but also involving new assay development and optimizations. You will work in a sub-team jointly led by a PhD student and senior technician. We look for a candidate comfortable in