484 computer-science-quantum "https:" "https:" "https:" "https:" "U.S" research jobs at Nature Careers
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! Education · PhD in computer science, engineering, applied mathematics, physics, or another STEM discipline. · Demonstrated experience with mathematical and numerical optimization methods
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, supported by advanced systems-level modelling and close collaboration with industrial and policy stakeholders. The successful candidate will contribute to the HyperCap research program focused on developing
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interdisciplinary, and together we contribute to science and society. Your role The Junior Research Group in AI in Biomedical Imaging conducts applied AI research focused on biomedical image computing. Our work
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on the topic outlined above is paramount Candidates are expected to be interested in working at the boundaries of several research domains Master’s degree in computational biology, bioinformatics, systems
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Job Advertisement Job-ID 03/2026 At the Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI), we investigate the pathobiology of microorganisms and develop new natural
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Doctoral (TV-L E13, 65 %) and Postdoctoral Researcher Positions (TV-L E13, 100 %) in Microbial Commu
or PhD (or equivalent) in Natural or Life Sciences (e.g., Biology, Chemistry, Bioinformatics, Geosciences, Biomedical Sciences, Biotechnology, etc.). Candidates about to obtain their degree are welcome
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission of teaching and
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at international conferences. You hold a PhD in computational biology/chemistry, machine learning or a related quantitative field. You have a solid publication record and demonstrated experience with advanced
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-derived organoids and assembloids, engineered ECM environments, and in vivo mouse models, working in close partnership with the lab's computational team to generate data-rich spatial multi-omics datasets