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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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main focus on our Computer-Aided Drug Discovery (CADD) platform. The successful candidate will work with a highly multidisciplinary team at VIB DS in close collaboration with a team of IT experts at VIB
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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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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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laboratory currently focus on: Detection of Advanced Persistent Threats (APT’s); Detection of cyber-attacks using machine learning and AI; Cyber training systems and cyber ranges; Prevention of phishing. What