402 machine-learning "https:" "https:" "https:" "https:" "https:" "Cardiff University" positions at Nature Careers in United States
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science, and educators to advance learning. We are proud to be part of progress, working together with the communities we serve to share knowledge and bring greater understanding to the world. For more
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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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Lavis Lab, please visit https://www.janelia.org/lab/lavis-lab About the role: In this role, you will support the vision of Open Chemistry by designing and synthesizing fluorescent dyes and other small
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visiting https://kinglab.berkeley.edu/ . About the LART role: You will provide administrative and technical support to the Investigator and laboratory staff in the King Lab at UC Berkeley, which studies
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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
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research and teaching interests, (3) up to three representative publications, and (4) the names and contact information of at least three referees. Applications must be submitted electronically at http
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senior research position to work on projects related to computational analysis of mass spectrometric datasets. A major focus will be on the application of AI/machine learning models and other computational
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interests in applied statistics, machine learning, or computational biology are encouraged to apply. For more information, please visit our website https://ds.dfci.harvard.edu/postdocs to view the list
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the HNSCC team, including Taran Gujral (machine learning-enabled drug screening), Slobodan Beronja (mouse models of HNSCC), and Patrick Paddison (functional genomics). This work will encompass a broad array