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-doctoral Associate will develop algorithms and theory for machine learning methods, as well as implement and apply ML methods to problems in domains such as computational biology and neuroscience. This is a
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the application of machine learning techniques (e.g., doc2vec, encoder models, multi-modal embeddings, large language models) to map concepts and their relationships, tracing how they change, merge, or diverge
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nuclear physics detectors. Experience analyzing data from high energy or nuclear physics experiments. Familiarity with Monte Carlo simulations. Familiarity with machine learning techniques. About the
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machine learning analyses will be performed to determine correlations across stimulation settings and body systems as well as to develop predictive models and biomarkers for physiological and clinical
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computer science programs (Chemical Engineering, Civil and Environmental Engineering, Computer Science, Electrical and Computer Engineering, and Mechanical and Industrial Engineering). This two-year
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demonstrated experience with a set of tools appropriate for working with large-scale data science including application of machine learning. In addition, applicants must have demonstrated leadership experience
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, or a related technical field. ● Strong programming skills in Python, Java, etc. ● Expertise in machine learning, neural networks, and deep learning ● Excellent writing and communication skills ● Highly
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/Statistics, Medical/Health Informatics. Strong computational and programming skills with abilities to develop cutting-edge large-scale machine/deep learning algorithms using high-performance computing (HPC
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conducting clinical or preclinical proof-of-concept studies Preferred: Experience in physiological signal processing and the application of machine learning to biomedical data Background in computational