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participant outcomes. The project will use a variety of approaches, including human perceptual experiments, machine learning, digital signal processing, and computational models of hearing. UConn has a vibrant
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at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable implementations. By establishing a new class of multi-frame factorization methods, the candidate will
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–functional modeling of root system architecture. Phenomics data integration and high-dimensional trait analysis. Predictive breeding and quantitative genetic modeling. Machine learning approaches to genotype
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 2 days ago
computer programs and learn debugging strategies. By the end of the course, students are expected to create a program that helps them solve a problem or perform a task (either self-chose or assigned) in
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in a university or college setting. Working knowledge of the content areas of probability, statistical methods, generalized linear models, statistical computing, and machine learning. Preferred
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an MD or PhD in Biochemistry, Neuroscience, Microbiology, Immunology, Genetics/Genomics, Biomedical Sciences, Biology, or a related field of study from an accredited institution or a terminal degree in
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Your Job: We are looking for a PhD student to develop learning-based surrogate models for predicting stress fields in patient-specific arteries. Especially high stresses in plaque can lead to
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the neurovascular space. Knowledge of neurovascular anatomy, acute stroke, endovascular treatments, neuroendovascular devices for the treatment of stroke. Ability to generate machine learning analysis of medical
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. 3 or more years of demonstrable experience in machine learning theory. Excellent teamwork and communication skills Fluent in English The candidate who has obtained the highest score in the selection
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AI / Machine learning / Computational Oncology lab:Our work is translationally focused, towards realizing our vision of developing new approaches for fast and low-cost prediction of patient response