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a 3-year DOE-sponsored project that started in September 2024. The Postdoctoral Research Associate working on P1 will develop and test deep learning algorithms for model emulation and model parameter
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. Implement, adapt, and evaluate ML and NLP algorithms for scientific and security applications. Work in interdisciplinary collaborations with subject matter experts from a variety of domain sciences and
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. His expertise includes: Developing novel deep learning and machine learning algorithms for biomedical and health applications. Applying responsible AI principles to ensure privacy-aware and
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quality algorithms and products (e.g., temperature, turbidity, chlorophyll-a) while developing novel methodologies to advance research on short- and long-term water quality conditions. Approaches may
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and relevant data analysis. • Demonstrated experience in Python programming. • Knowledge of machine-learning algorithms. Additional Information: BNL policy requires that after obtaining a PhD
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learning field. Excellent programming and computer science skills. Preferred Knowledge, Skills, And Abilities Practical experience developing novel ML, LLM, or CV algorithms and models. Experience with state
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imbalanced cross-sectional imaging data are especially well-suited for the position . The postdoctoral researchers will develop algorithms for deployment at our prestigious R Adams Cowley Shock Trauma Center
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properties of macromolecules, developing novel ways to combine quantum chemical methods and machine learning, developing quantum algorithms for computational chemistry on quantum computers, and applying
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required. Required Qualifications: Expertise in one or more of the following areas to solve problems in computational epidemiology as they relate to HAIs: AI, algorithms, discrete optimization, data mining
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linear mixed models, and biological network analysis. This may require development of new computer algorithms and/or construction of computational workflows on computer clusters. The types of multi-omics