257 machine-learning "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" "U.S" Postdoctoral research jobs at Nature Careers
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: https://obgyn.uchicago.edu/research/griffith-laboratory Required Qualifications: Qualifications needed for this position include a PhD in clinical psychology or a related field, such as psychology
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a postdoctoral scholar in computational biology. The PI, Dr. Lixing Yang is an Associate Professor at the Ben May Department for Cancer Research and the Department of Human Genetics. To learn more
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understanding and generation, media forensics, anomaly detection, multimodal learning with an emphasis on vision-language models, computer vision applications for space. Key responsabilities: Shape research
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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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methodology. Applying AI and machine learning (ML) tools (including Python, R, and possibly other languages) to test and evaluate biomedical hypotheses. Developing benchmarks and working together with staff
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T cell biology or cancer immunology, and programming skills (R, Python) for data analysis. Please also read recent manuscripts published in the last two years 2024 Nature: (https://www.nature.com
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publication record in immunology/epigenetics. Information on our postdoctoral training program, benefits, and a virtual tour can be found at http://www.utsouthwestern.edu/postdocs . Please also read recent
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, sex, pregnancy (including childbirth and related conditions), disability, genetic information, status as a U.S. veteran, service in the U.S. military, sexual orientation, or associational preferences.
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Aarhus University (http://bio.au.dk/en) and work in the Archaea Group (https://bio.au.dk/en/research/research-areas/microbial-processes-and-diversity/archaea-group), Section for Microbiology
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Postdoctoral Positions for Computational Genomics, Cancer Genetics, and Translational Cancer Biology
mechanism-driven AI and agentic AI frameworks (iGenSig-AI, G2K) that integrate biological knowledge with cutting-edge machine learning to transform omics data into actionable therapeutic insights