84 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at University of Minnesota
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, computer vision in the Division of Health Data Science (HDS) at the DOS. The position is an annually renewable professional academic appointment. Duties/Responsibilities: ● Risk predictive model for clinical
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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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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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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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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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/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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. This position is not eligible for H-1B visa sponsorship. Qualifications Required: PhD in Theoretical Physics by beginning date of appointment Preferred: Expertise in modern aspects of non-perturbative quantum
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. Qualifications Required Qualifications (must be documented on application materials): • PhD in Biology, Chemistry, or related Biomedical Sciences • First author and co-authored publications • Project leadership
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) community-engaged research methodologies; (6) program or policy evaluation; and/or (7) implementation and dissemination. Ample opportunities exist to develop an independent program of research in one or more
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that adhere to University policy. Assist other lab/community members, as needed. Qualifications Required Qualifications PhD in Structural Engineering, Civil Engineering, or closely related field Preferred