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antigens, T cell receptor (TCR) and antigen interactions and their crucial role in anti-cancer immune responses. You'll leverage your strong background in computational biology, machine learning, and
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work that underpins the scientific research of the collaboration. Research Title: Coupling Computation and Machine Learning to evaluate PFAS Chemicals The work will entail: The position is for a
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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
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School of Engineering and Applied Sciences at Harvard University invites applications for a postdoctoral position in robot learning, beginning in September 2025, or soon thereafter. The position is for one
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University's Faculty of Arts and Sciences to study spatial and temporal aspects of interaction patterns in biological systems. We are looking for exceptional candidates with background in machine learning and
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collaborative, impact-focused problem solver who wants to be part of a dynamic team. Information about the Shih Lab: Learn more about the innovative work led by Dr. William Shih here: https
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the High Performance Computing and Data Center, to be completed on campus by August 2026. This position collaborates with others in the growing machine learning and exoplanet subgroups within the Physics and
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bone marrow transplantation. We are looking for a highly motivated candidate with a PhD degree interested in using microbiological methods with the aim of developing novel strategies to improve bone
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thrombosis and lung injury in Sickle Cell Disease. The prospective candidate will have the opportunity to learn state-of-the-art techniques such as Multi-Photon-Excitation intravital microscopy of the lung and
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 1 month ago
Kannapolis and UNC Charlotte in Charlotte. Experience in yeast genomics, RNA-sequencing, bioinformatics, or machine learning is preferred. Departmental Preferred Experience, Skills, Training/Education