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Field
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Associate in PFAS Predictive Modeling with the University of Maine Cooperative Extension will establish and sustain a dynamic research and educational outreach program to address data and modeling needs
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should be prepared to integrate internal experimental data with public protein databases such as UniProt, PDB, Ensembl, BioPlex, DepMap, and others. Responsibilities : Develop and maintain a centralized
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with Dr. Emily Becker and the NOAA Climate Prediction Center. Key responsibilities Train neural networks and quantify uncertainty to evaluate predictability Perform explainable AI (XAI) related research
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superconducting behavior, utilizing NV centers in diamond and spin defects in 2D materials in variable-magnetic-field cryogenic environments. The candidate will leverage single- and ensemble-spin based sensors
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and computational studies of mechanics of materials, with an emphasis on fracture prediction. In addition, the postdoc will contribute to and/or lead manuscript preparation and project reporting
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disparities in supportive care medication (SCM) use, using national datasets (e.g., SEER-Medicare, SEER-MHOS), predictive analytics, and stakeholder-engaged methods. The lab is committed to actionable research
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candidate will work on cutting-edge research integrating genome-scale language models (GenSLMs) with deep mutational scanning data, and experimental virology to predict viral evolution and identify emerging
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promoters. You will train and evaluate predictive models in model/crop species with different levels of genome complexity. You will work very closely together with your dry-lab colleagues for data processing
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single-cell profiling and predictive artificial intelligence models, you will engineer synthetic promoters controlling context-specific gene expression in Arabidopsis. You will develop high-throughput
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. Depending on the candidate’s interest, this position will focus on at least one of the following areas: (a) developing the technique to improve the prediction of aerosols and traces gases via data