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numerical methods, as well as familiarity with concepts in complex systems, physical memories or machine learning. We strongly believe in the benefits of an inclusive and diverse research environment, and
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crucial; Design interventions to reduce bias and improve fairness and safety in human-AI interaction. The research will combine computational modeling (e.g., NLP, machine learning, deep learning) with human
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emotion safety is crucial; Design interventions to reduce bias and improve fairness and safety in human-AI interaction. The research will combine computational modeling (e.g., NLP, machine learning, deep
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development in coordination with the interests of its scientists. As such, there are plenty of opportunities to learn new skills, expand your knowledge, collaborate across disciplines, and experiment in a
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of machine learning to evaluate the predictive value of biomarkers from various sources: donor-related data, perfusion fluid, and kidney biopsies. Kidney biopsies may contain unique information about organ
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opportunity to tackle these two complementary perspectives. In the first direction, you will develop advanced system identification techniques that combine nonlinear dynamics theory with machine learning tools
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. Nice to have: Practical experience with machine-learning frameworks (e.g., PyTorch). Prior tape-out experience (ASIC or a complex FPGA prototype) and familiarity with the digital back-end flow (synthesis
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these sounds fascinating, then this PhD position is made for you! Information We invite highly motivated students with a strong background in mathematical control theory, and a keen interest in machine learning
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for the project team. We are looking for a student with a strong background in at least one of the following subjects: statistics, machine learning, discrete optimization, operations research. A strong interest in
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quantitative modeling; Strong expertise in programming, including proficiency in languages commonly used in data analysis and machine learning, such as Python; Excellent verbal and written communication skills