412 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" positions at Nature Careers in United States
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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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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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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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, or methodologies in bioengineering. This search has a particular focus in immunology, neuroscience, and/or computational science/machine learning. That said, we give high priority to the overall originality and
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Assistant Professor in Marine Biology & Ecology - Biomedical Science or Quantitative Systems Ecology
ecologist working in coastal systems, who applies modern approaches in causal inference, experimental ecology, spatial modelling, and data science, including the use of machine learning to produce rigorous
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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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support as Chancellor Scholars. Academic rank and salary will be commensurate with qualifications and experience. For details and to submit your application, visit https://phri.njms.rutgers.edu/ and https
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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