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Previous Job Job Title Post-Doctoral Associate Next Job Apply for Job Job ID 369579 Location Twin Cities Job Family Academic Full/Part Time Full-Time Regular/Temporary Regular Job Code 9546 Employee
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation increments, which represent structural model errors (https://doi.org/10.1029/2023MS003757
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 15 hours ago
developing software and analytic tools to perform disease modeling and critical analytics in response to infectious disease outbreaks. ACCIDDA is part of the newly formed CDC-funded national network of centers
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multiple brain regions interact with each other, or networks. Furthermore, we are focused on identifying factors affecting the plasticity of these representational networks, and the degree to which they can
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QUALIFICATIONS Track record of mentoring and teaching laboratory and professional skills. Experience using code-based programs (Python, MATLAB) for processing and analyzing large or multimodal datasets (e.g
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projects ranging from score-based generative models, energy-based models, Bayesian analysis of graph and network structured data, highly multivariate stochastic processes; with data applications ranging from
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. amino acid and lipid metabolism), signaling pathways (e.g. mTOR and nutrient signaling), and systems-level regulatory networks that regulate basic T cell and dendritic cell biology and antitumor immunity
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Open Date 07/17/2025 Requisition Number PRN42561B Job Title Post Doc Res Assoc w/Ret Working Title Post Doc Res Assoc w/Ret Job Grade A00 FLSA Code Administrative Patient Sensitive Job Code? No Standard
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) experience with neural network training and language model fine-tuning; (2) background in natural language processing, linguistics, and/or human reasoning; (3) strong coding skills; and (4) strong
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data analysis for omics studies, including genotype data, bulk and single-cell RNA sequencing data, high-dimensional phenotype data, pathway and functional analysis, network analysis, polygenic risk