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on human behavior modeling related to video classification using deep learning networks for end-users. Work with other team members to develop and maintain software for maximum efficiency and usability
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have good knowledge of machine learning techniques, ideally deep learning; you have strong programming skills in Python, MATLAB, or C++, including deep learning frameworks (e.g., PyTorch); you have the
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to SAFE. Delivering EVU course from SAFE center. Required selection criteria A PhD degree (or equivalent) in biometrics, information security, computer science, electrical engineering, or machine learning
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Information regarding postdoctoral fellow salary, which is determined by the number of years post PhD, can be found at https://postdoc.hms.harvard.edu/guidelines Minimum Number of References Required Maximum
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or a Scandinavian language is also beneficial. The following are considered beneficial: solid theoretical background in robot perception and navigation deep foundation in modern machine learning solid
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possess a PhD or be close to completion of a doctorate in accounting from an AACSB accredited University. The ideal candidate is expected to conduct high-quality research, teach both undergraduate and
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(“overparameterized”) machine learning models, like probabilistic graphical models, deep neural networks, diffusion models, transformers, e.g. large language models, etc. SLT is based on the geometrical understanding
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closely related quantitative discipline. Demonstrated experience with large-scale deep learning models and modern ML frameworks (e.g., PyTorch, JAX, Transformers), including training, fine-tuning
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, and ecological plant physiology. Students acquire advanced methodological approaches and participate in international scientific cooperation through scientific meetings and internships at foreign
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Machine Learning. Profile of the graduate The graduate displays deep theoretical knowledge in molecular and cell biology, genetics and virology, with focus on some specific branch of these scientific fields