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Field
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conducts research on the application and the impact of digital technologies like DLT/Blockchain, Digital Identities and Machine Learning/AI on organisations from both the private and public sectors
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twin of sperm motility, and utilize it to develop a separation method. Your tasks will include: Performing computer simulations and matching them to experimental data Very close collaboration with
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morphology (e.g., geometric morphometrics, machine learning), and phylogenetic comparative approaches. We have: • An engaging, supportive, and collaborative research environment. • Opportunities
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machine learning are encouraged to apply. The postdoc will also play a leading role in co-mentoring graduate and undergraduate students, assist in organizing seminars and activities related to RTG
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, reporting research progress. Specific Requirements Requirements: PhD degree in biotechnology, biology, or a related discipline obtained after 2023. Very good command of English and excellent communication
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) Corrosion behavior (electrochemistry & high-temperature oxidation) In-situ monitoring of AM processes Computational skills in: Phase-field modeling, Machine Learning, FEM, DEM, COMSOL Alloy design (CALPHAD
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biology, and evolution. Learn more about our interests, motivations and discoveries: https://sites.duke.edu/silverlab/ . Conduct independent research activities under the guidance of a faculty mentor in
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: Responsible for performing organic reactions in Dr. Sielecki’s lab within the Institute of Advancing Medical Innovation and the Department of Cancer Biology. Post-docs will learn methods appropriate
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] Subject Areas: Physics / Hard Condensed Matter Theory , Machine Learning , Material Science , Physics , Quantum Information Science , Soft Condensed Matter Theory , theoretical condensed matter physics
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social or behavioral sciences (incl. human-computer interactions with relevant experience). Applicants must demonstrate experience in experimental work with human participants; possess versatile