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University of Massachusetts Chan Medical School | Worcester, Massachusetts | United States | about 2 months ago
biology as a high-dimensional, dynamic, networked system, applying techniques from machine learning, causal inference, statistics, and algorithms. No prior biomedical training is required—just strong
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cellular biology, electrophysiology, imaging, genetic and whole-animal cardiovascular assays. This position represents an opportunity to work side-by-side with talented graduate students, postdocs, and
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considered for this opportunity. Start date is Fall 2026, but some flexibility is possible. Postdoc is expected to be in person at Stony Brook University. The position is for two years, with the second year
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on funding availability. The position will be for FY26 Level 0 or Level 1 Postdoc. Key Responsibilities: · Research (90%) Development of data analysis plans, soliciting input from the team and carrying
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considered for this opportunity. Start date is Fall 2026, but some flexibility is possible. Postdoc is expected to be in person at Stony Brook University. The position is for two years, with the second year
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30 Jul 2026 Job Information Organisation/Company University of Aveiro Department SGRH Research Field Chemistry » Biochemistry Other Researcher Profile First Stage Researcher (R1) Positions Postdoc
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7 Jul 2026 Job Information Organisation/Company Université de Caen Normandie Research Field Psychological sciences Researcher Profile First Stage Researcher (R1) Positions Postdoc Positions
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to staff position within a Research Infrastructure? No Offer Description Department of Forest Mycology and Plant Pathology A 2-years postdoc position is available in the Soil Microbiology group
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from molecular level to animal experiments. Mathematical processing of the results. Performing parametric and non-parametric statistical analyses. Interpretation of data considering the possibility
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and systemic health using an evolutionary health framework. There will be a specific emphasis on applying advanced bioinformatics and statistical approaches to large, population-level datasets in