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seeks to enhance the robustness and efficiency of debiased machine learning methods. This PhD project will primarily focus on this latter component, in interaction with fellow researchers on this project
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of older persons. The candidate will also contribute to teaching activities related to machine learning or other areas depending on the candidate’s profile. Moreover, the candidate is a team player that
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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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within the IN-DEEP project you will be at the forefront of developingnew hybrid machine learning (ML) accelerated solvers. A fast-expandingarea of research is the application of ML techniques to predict
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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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of debiased machine learning methods. This PhD project will primarily focus on the foundations of an assumption-lean modeling paradigm, with a strong focus on the analysis of repeated measures outcomes, in
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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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Dept. ESAT of KU Leuven (Belgium) in the frame of the AI initiative of the Flemish Government. The goal of this research is to develop new machine learning methods for data-driven selection and