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Field
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discovery machine for unlocking the secrets of the "glue" that binds the building blocks of visible matter in the universe. The machine design is based on the existing and highly optimized RHIC Ion-Ion
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collaborative with other team members. The postdoctoral candidates will benefit from the interaction with the diverse and interdisciplinary environment at the University of Arizona College of Medicine - Phoenix
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Institute for Medical Research, and the German Cancer Research Center (DKFZ). Strong interactions exist via the Health + Life Science Alliance Heidelberg Mannheim with the European Molecular Biology
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two PhD students in the groups, and your newly developed tools will support the analysis and interpretation of their data. You will interact strongly with several international collaborators, both
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care policy and aging research. Our faculty are teachers, clinicians, and scientists with unparalleled experience who advance medical innovation and provide patient care informed by the latest research
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processing and integrating massive multi-omics datasets from clinical cohorts. Key Responsibilities: Design and implement AI/ML pipelines for multi-omics data integration, including supervised and unsupervised
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interactions with experimental collaborators. Many of the problems are constrained by inherently low-quality or noisy data, and the successful candidate will be enthusiastic about contributing to data
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candidate will interact with a team of scientists and engineers at ORNL on projects evaluating sustainable energy metrics, such as costs, benefits, equity, land use change, and carbon reduction potentials
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and innovations and work flexibly across laboratories as required. Analyse and record data to aid the design of future research Take responsibility for self-development and on the job learning; keeping
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of design, computation, and robotics. ARG's research interests include topics such as robot learning, human-robot interaction, Generative AI, computer vision, closed-loop control, extended reality (XR), and