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and predict how the immune system responds to interventions. This tight integration of advanced machine learning and experimental immunology allows us to tackle fundamental biological questions with
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and predict how the immune system responds to interventions. This tight integration of advanced machine learning and experimental immunology allows us to tackle fundamental biological questions with
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learning as well as a strong background in scientific programming (in languages like Julia, Python, Fortran or C/C++). The applicant must hold a PhD in physical oceanography, atmospheric sciences, computer
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a sequential decision-making process explored through computational simulation and deep multi-objective reinforcement learning. The project will investigate a simulation platform that reproduces
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reinforcement learning. The project will investigate a simulation platform that reproduces the structure of real prospection activities by integrating multiple, heterogeneous geospatial and archaeological data
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researchers with PhD in one of the following fields: electrochemical engineering; chemical engineering; materials science; battery science; computational materials science; applied physics; mechanical
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-scale nature, complexity, and heterogeneity of 6G networks, we use tools such as artificial intelligence/machine learning, quantum computing, graph theory, graph-signal processing, and convex/non-convex
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your research in at least one high-impact A1-journal (ISPRS, ICCV, etc.). You will (co-)author a project proposal (FWO, Industry) to continue this research. You have a PhD on a relevant topic in
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hold a PhD in machine learning, computer science, bioinformatics or equivalent. You combine strong analytical skills with the ability to work independently and lead collaborative efforts. You are a team
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-level modelling of environmental exposures and health risks; Interpretable and uncertainty-aware machine learning for heterogeneous health data. This position offers the opportunity to work in a