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Field
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for relevant systems of EF-hand proteins · Supervise and instruct graduate and undergraduate students in Dr. Pengfei Li’s lab Minimum Education and/or Work Experience A MD or PhD is required in a closely related
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research in various areas of mobile network systems, multimedia and AR/VR/XR systems, robotics and machine learning, focusing on fundamental aspects as well as on applications in multidisciplinary contexts
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dataset analysis, machine learning tools, and relevant computational biology approaches • Document, compile, and format data analysis in presentations and reports to supervisor. • Mentors and trains
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information-security approvals required by the programme, and engage with internal University stakeholders (IT, HR, Teaching & Learning, faculties) and external partners. We welcome candidates who bring diverse
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extensive knowledge of robotic navigation, machine learning or other relevant fields experience working with marine robotic systems or the data they collect, including inertial, acoustic, visual and
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of future reactor systems with a focus on systems relevant for Norway. The objective is to further develop and validate machine-learning surrogate models derived from high-fidelity multiphysics simulations
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assigned by Supervisor. Requirements PhD in Computer Science or related field Expertise in computer vision and vision-language models Experience with ML evaluation metrics and benchmarking Proficiency in
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of metabolism, membrane transport, cellular regulation, structural systems biology, machine learning and disease mechanisms. These positions are embedded in the transition from CeMM in Vienna to the newly founded
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project “Actively learning experimental de-signs in terrestrial climate science (ACTIVATE)”: https://www.mn.uio.no/geo/english/research/projects/activate/index.html The PhD fellow will be part of a growing
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mathematical structures and computational algorithms underlying modern machine learning and artificial intelligence. Relevant themes include geometric and algebraic methods for learning, structure-preserving