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related field. Documented expertise in machine learning and time-series modelling (e.g. LSTM, XGBoost, CNN). Strong programming skills in languages such as Python and R. Experience with phenotyping data
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groups working on digital health and wellbeing , network science , computational social science , and various topics in machine learning. You will be working in the research group of one of the PIs
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biosciences and data analyses. Qualifications Basic qualifications (required at time of application) PhD (or equivalent international degree) or enrolled in a PhD (or equivalent international degree) program
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systems Strong skills in data-driven analysis and modelling, simulation, control, and validation Familiar with modeling of PtX and storage technologies, model predictive control, machine learning
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Acceleration who has: strong experience in FPGA design, machine learning or a related field in the case of the Postdoctoral Research Associate, a PhD (or near completion) in FPGA design, machine learning or a
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machine learning methods. The successful candidate will lead an independent research project dedicated to identifying abnormal behavior and neuronal activities in circuits of murine models of 22q11.2 and
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machine learning methods in the context of biological systems Experience with programming (e.g., Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will be hosted
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Additional Information Eligibility criteria Transversal knowledge required : - Expertise in machine learning and deep learning in particular - Knowledge in ecology, marine biology, or oceanography would be a
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collisions and maximize efficiency through innovative AI-based movement and maneuver planning. For the first time, innovative machine learning concepts, such as “shadow learning”, are being used. Appropriate
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PhD in Data Science, Computational Social Science, Computer Science, or Information Science. The position requires experience with at least one of the following: Data Science, Machine Learning