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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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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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-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
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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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with machine learning image processing approaches Experience in CRISPR/cas9/cas12a for human ES cell gene editing or in vivo screening designs What we offer The initial appointment is for 2 years, with
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generation and integration, gene regulatory network reconstruction and wide range of machine learning approaches The host labs will provide financial support for the whole length of the PhD. The applicant will