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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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data processing, photogrammetry, computer vision, and big data/machine learning. You have knowledge of fundamental principles, algorithms of computer vision, and machine learning methods. You have
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, model development (incl. machine-learning techniques) and validation (based on data from the 2021 European mega-flood), application to real-world case studies in Belgium and Germany. Modelling
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on the topic the development of a machine learning-based powertrain digital twin and smart preconditioning for fast-charging applications. Tasks include: Simulation of the map-based and data-driven powertrain
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available to acquire more The ICTEAM is host to 50 professors and more than 200 researchers in computer sciences, applied mathematics, and electronics, it is a very innovative ecosystem with all expertise
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an interest in machine-learning applications in experimental particle physics. The exact research activities to be pursued will be defined with the selected candidates and could extend beyond these main topics
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the organization of workshops, lecture series, and dissemination of your research to the broader public. WHAT WE ARE LOOKING FOR You hold a thesis-based doctorate in computer science, machine learning, or related
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design (and machine learning). You have experience in probabilistic methods (and machine learning). You have proven experience in Python. Soft skills You have a team player mindset, a strong personality
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Machine Learning approaches (FLUXCOM) for the Belgian domain, that will be used as prior for atmospheric inversions. You will interact with the similar international initiatives on emission verification in