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an active role in other projects and activities within the Smart Materials Lab and assist in supervising undergraduate or PhD students. Applicants must hold a PhD with at least 3 years of experience in
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the period of the project, not exceeding the maximum period set by FCT for such grants. RENEWALL Renewable is subject to performance if the candidate is enrolled in a PhD program - art. 6º, n.4 c) https
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are particularly interested in candidates with extensive finance industry experience and a background in quantitative finance, statistics, data science, machine learning, or artificial intelligence. This is a Haas
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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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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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dynamics simulations is highly desirable. Basic knowledge of machine learning is considered an advantage but is not mandatory. LanguagesENGLISHLevelExcellent Additional Information Work Location(s) Number
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Infrastructures Didactics of Informatics Digital Humanities Distributed Systems High-Performance Storage Machine Learning Medical Informatics Neural Data Science Practical Informatics Scientific Information
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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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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 3 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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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