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collaboration with Professor Rafael Perera-Salazar (Nuffield Department of Primary Care Health Sciences, University of Oxford). You should possess a relevant PhD (or near completion*) in Engineering, Mathematics
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are especially excited to hear from candidates eager to invent new tools, wetlab approaches for circulating nucleic acids, interpretable machine learning for biomarker discovery, and methods we haven’t imagined
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(there is room for learning-on-the-job). A PhD in aerospace/mechanical engineering or applied physics. Demonstrated ability to conduct research in experimental fluid mechanics. Proven competence on flow
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machine learning for molecular and material design; quantum computing for bioinformatics; quantum approaches for safe and sustainable molecular design; and benchmarking quantum simulations of materials
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will possess a PhD or equivalent doctoral degree in an engineering or physical science discipline with some research experience related to critical minerals. They must be able to communicate
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About the Opportunity Conduct research on machine learning, control theory, and synthetic biology. The work will combine tools from dynamical systems, control theory, and the theory of algorithms
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scientists wanting to transition from PhD student to junior group leader. It supports young scientists in exploring their own ideas and testing new hypotheses. High-risk, high-gain projects are encouraged
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(payable 14 times per year) Responsibilities The applicant is expected to establish an own research group with focus on advanced machine learning and deep learning techniques for remote sensing applications
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Qualifications • PhD in Biomedical Engineering, Mechanical Engineering, Electrical Engineering, Robotics, or a related engineering field. • Experience in robotics design, mechatronics, electromechanical systems
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structure modeling in cancer immunotherapy design. Profile A — AI PhD in machine learning, computer science, computational science, or a related field. Strong experience with deep learning (e.g., PyTorch