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marrow transplantation and CAR T cell therapies, spanning basic science to clinical trials. For more information about Dr. van den Brink’s research, please visithere. Lab's Philosophy : We foster a
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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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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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, molecular dynamics, and machine learning, to model battery electrolyte and solid electrolyte interphase (SEI), while collaborating with experimentalists. Qualifications • Ph.D. in Computational Materials
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Desirable criteria Experience of advanced statistical and/or machine learning methods, such as longitudinal analysis methods, latent variables models, clustering algorithms, missing data and clinical trial
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projects as well as general research involving the application of methods from theoretical physics, mathematics, and machine learning with the goal to understand the brain function. Postdoctoral Fellowships
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as world-leading or internationally excellent. We use this expertise to teach the next generation of health professionals and research scientists. Based across King’s Denmark Hill, Guy’s, St Thomas
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discoveries. Who You Are: Ph.D. with a proven track record of excellence in Computer Science and Machine Learning, with substantial domain experience in biology and genomics. Must have advanced at least one key
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, computational, and machine learning/AI methods, with a particular emphasis on deep learning approaches improve our understanding and prediction of infectious disease dynamics. Projects are also strongly grounded
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spatial transcriptomics and imaging genomics projects, integrating bulk and single-cell RNA-seq datasets, and applying advanced statistical and machine-learning methods (AI/ML) to extract novel biological