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-generating mechanism, integrating it with recent insights from debiased machine learning and causal inference. Besides laying foundations for a novel paradigm for causal/statistical modeling, 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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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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insights from debiased machine learning and causal inference. Besides laying foundations for a novel paradigm for causal/statistical modeling, this project seeks to enhance the robustness and efficiency
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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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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
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Description We are looking for several highly motivated PhD candidates with a background in photonics and an interest in machine learning or in combinatorial optimisation, for several research projects in