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The University of Texas MD Anderson Cancer Center | Houston, Texas | United States | about 21 hours ago
, tumor immunology, preclinical therapeutic studies is highly desirable. Previous computational expertise is not required, although an interest in interpreting high-dimensional molecular data and
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and neuroimmune regulation in autoimmune and cardiometabolic disease. The lab integrates human genetics, single-cell multi-omics, computational biology, and experimental immunobiology to understand how
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are derived from plant breeding, genomics, quantitative genetics, physiology, biotechnology, computational biology, and horticulture. You will be encouraged to establish robust collaborations with faculty
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USDA-ARS Molecular Biology Postdoctoral Fellowship in the Natural Products Utilization Research Unit
available to U.S. citizens only. ORISE Information: This program, administered by ORAU through its contract with the U.S. Department of Energy (DOE) to manage the Oak Ridge Institute for Science and Education
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advance pediatric, adolescent, and young adult cancer care using cutting-edge computational biology approaches. We strive to understand the germline genetics and tumor genomics of pediatric cancer to inform
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differential geometry, algebraic geometry, etc.) or for computer science (such as machine learning, linear logic, etc.).While the position start date is flexible, the successful applicant must have completed
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of human activity and climate change. This position is located at FAU's Harbor Branch campus in Fort Pierce, FL, and is funded by the NOAA ECOHAB program and HBOIF. The appointment will be for two years with possible
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differential geometry, algebraic geometry, etc.) or for computer science (such as machine learning, linear logic, etc.). While the position start date is flexible, the successful applicant must have completed
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-based experimental neutrino program. This fellowship includes competitive salary and benefits, plus US $10K of research and travel support per year for 3 continuous years, depending on the availability
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learning, and computational biology to adapt and augment existing open models for predictive biology. Approaches may include fine-tuning, parameter-efficient tuning, probing, and retrieval- or knowledge