18 computer-science-"https:"-"ESPCI-Paris---PSL" "https:" "https:" Postdoctoral positions at Nature Careers in United States
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. Applicants should hold a PhD in Computational Biology, AI/ML, Computer Science, Population Genetics, Bioinformatics, or a related field, and have fewer than five years of postdoctoral experience. Strong
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gained to improve the sustainability of agriculture and the climate change resilience of crops. The Maere lab at PSB ( http://www.maerelab.be ) is active in the fields of computational biology
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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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/articles/s42256-024-00821-x, https://pubs.acs.org/doi/10.1021/acs.analchem.5c06256, https://www.nature.com/articles/s41570-023-00570-2). The research is computational in nature but involves close
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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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peptide, mRNA, or gene therapy development related to mitochondrial disease. Computational biology / bioinformatics, especially ribosome profiling, disease gene discovery, or integrative multi‑omic
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preparing results for publication. Qualifications MINIMUM QUALIFICATIONS: PhD (or equivalent) in biology, data science, computer science, bioengineering, physics, or a related field. A strong publication
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Postdoctoral Positions for Computational Genomics, Cancer Genetics, and Translational Cancer Biology
Postdoctoral Positions: Computational Genomics · AI-Driven Precision Oncology · Translational Cancer Biology Wang Laboratory, UPMC Hillman Cancer Center Department of Pathology and Human Genetics
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. The candidate will lead computational analyses of these datasets, using the laboratory’s suite of existing AI/ML tools to assign structures to unidentified peaks in metabolomic datasets (e.g., https