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statistical genetics techniques to analyse large-scale single-cell and genomic data. Data Analysis: Process, analyse, and interpret high-throughput single-cell and genomic data to derive biologically meaningful
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Position Details Position Information Announcement Number STAFF - VA - 26221 For questions regarding this position, please contact: Kelley Kane kelleykane@montana.edu 406-994-5097 Classification
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experience working with large, complex data from varied sources. The candidate should have proficiency with statistical software, ideally Stata, strong written and interpersonal communication skills, and a
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of thousands of individuals, large-scale single-cell data from primary human tissues, spatial transcriptomic data, and experimental genomic screens. The postholder will join a multidisciplinary team based in
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, BLIP), fine-tuning large language models for clinical NLP, and self-supervised contrastive learning—the models will learn to effectively combine visual and textual information. By developing
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outstanding research in computational physics for modeling of complex physical phenomena. Additional Qualifications Candidates must have experience in carrying out large-scale computational modeling of complex
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making and number sense, as well as how these processes are impaired in autism spectrum disorder, using custom-built virtual reality behavioral tasks in mice. Our major approaches include large-scale in
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include the preparation of samples in ultra-clean laboratory, isotopic analyses using multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS), data reduction, and U–Th age calculation
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researchers in population multi-omics, particularly in the domain of cardiometabolic diseases. Part of this position will involve working with Dr. Pankow and Dr. Peterson on analyses and manuscripts using data
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-Doc will process large meteorological and emission datasets, prepare input data for the models, and contribute to scientific publications. Requirements: PhD degree in physics/mathematics