98 phd-rehabilitation-engineering-computer-science Fellowship positions at Nature Careers
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to link microbial communities to climate-driven biogeochemical shifts. You hold a PhD in Marine Biology, Microbial Ecology, or related fields, have a strong background in omics data analysis (R
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approaches to this research. Whether you're passionate about information systems, business, law, computer science, medicine, sociology, biomedical engineering, psychology, or any other related discipline, we
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found online: https://www.jglab.org/ and www.github.com/goekelab Requirements: PhD in Bioinformatics, Computer Science, Statistics, Genomics, or a related discipline Good programming skills and strong
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models, and animal models of cardiomyopathy, heart failure and vascular disease. During past years, the lab has published in Nature Cell Biology, Developmental Cell, Development, Genome Biology, Nature
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have A PhD in biological sciences with demonstrated experience in computational biology and bioinformatics. The motivation and dedication to lead a scientific project. Excellent communication and
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Wellbeing Across Lifespan (SWAN) brings together expertise from the fields of medicine, life sciences, social sciences as well as computer and computational sciences to promote scientific and societal impact
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and beyond. Candidate Profile We welcome applicants with diverse backgrounds in experimental or computational biology who are excited by pioneering research directions. Qualifications PhD in molecular
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and synthesis of new materials. You should have a PhD in a relevant field (Computer Science, Mathematics are most likely to fit the role, but we are open to Chemistry, Materials Science, Chemical
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: · PhD or M.D. with a strong background and profound knowledge in the field of either molecular biology, immunology, cancer biology, experience in sequencing or protein structural data analysis is highly
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organoids will be plus. Dry lab: Highly motivated candidates with a PhD/MD degree in bioinformatics, genome science, systems biology, biomedical informatics, computational biology, machine learning, data