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University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 2 months ago
; proficient with Unix/Linux. Experience with deep learning, statistical modeling, or AI applications to biological data. Familiarity with transcriptomic technologies, ideally single‑cell RNA‑seq or long‑read
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integrative part of sustainable and healthy diets. Development of analytical methods, digestion models, dietary modelling, and conducting consumer surveys will form part of the Doctoral Network’s tasks. The 12
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of analytical methods, digestion models, dietary modelling, and conducting consumer surveys will form part of the Doctoral Network’s tasks. The 12 PhD candidates will be based across seven different universities
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behavior of pests, their response to pesticides, including physiological and genetic mechanisms of resistance, innovations in applied pest management research (e.g., molecular, computational modeling
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Director. · Work closely with the Xenotransplantation branch of the core facility to fulfill research needs of the users (maintain and expand current model/service portfolio based on user need). · Develop
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integrative part of sustainable and healthy diets. Development of analytical methods, digestion models, dietary modelling, and conducting consumer surveys will form part of the Doctoral Network’s tasks. The 12
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of analytical methods, digestion models, dietary modelling, and conducting consumer surveys will form part of the Doctoral Network’s tasks. The 12 PhD candidates will be based across seven different universities
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research teams to develop the necessary experimental platforms, modelling and data analysis capabilities. Research Assistant Professor (25260446) Responsibilities: The Department is recruiting one scholar
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of infection using metagenomics, advanced statistical models, and analytical biochemistry. One position to work on an NIH-funded project is available. The project is focused on determining the impact of
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with chemical descriptors to enhance uncertainty robustness Building Uncertainty-Aware General Purpose Models Merge developments into a unified, efficient MLFF architecture Train MLFFs on large, diverse