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/Deep Learning: ML/DL model development (e.g., PyTorch, Lightning) for predicting antigenicity, genetic variation, and viral evolution. Genomics & Bioinformatics: Viral genome-based evolutionary analysis
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on healthcare data. - Experience in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL) particularly in Natural Language Processing (NLP) and Computer Vision (CV) - strong record
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economic, energy efficiency, and environmental performance metrics. Utilize reinforcement learning (RL) and deep reinforcement learning (DRL) for autonomous process management, dynamic resource distribution
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University of Massachusetts Chan Medical School | Worcester, Massachusetts | United States | about 2 months ago
Minimum: USD $62,232.00/Yr. Salary Maximum: USD $75,564.00/Yr. GENERAL SUMMARY OF POSITION: Postdoctoral positions in Deep-Learning Omics are available in the Zhou Lab (https://profiles.umassmed.edu/display
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/recruitment/2-year-postdoctoral-p… ** Project ** Computational and high field MRI characterization of learning and decision-making ** Supervisor and contact ** Dr Florent MEYNIEL https://www.unicog.org/lab
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/Qualifications Strong research track record in AI and scientific applications. Excellent knowledge of Machine Learning and Deep Learning. Strong Python programming skills. Experience with PyTorch, TensorFlow
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, the Peabody Museum, or more technical parts of the University for periods of time to learn about both research and operational workflows. Connections with the Wu Tsai Institute, the AI at Yale program, the Data
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. Experience with deep learning and programming, preferably in Python, are required and should be evident from your academic track record, including the (online) courses you've followed, your publications
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concentration/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models