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. The project focuses on the intersection of deep reinforcement learning, probabilistic modeling, and bio-inspired architectures (such as Spiking Neural Networks) to achieve sample- and energy-efficient robust
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structure modeling in cancer immunotherapy design. Profile A — AI PhD in machine learning, computer science, computational science, or a related field. Strong experience with deep learning (e.g., PyTorch
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mathematical models to address fundamental questions in biology. Examples of research topics include but are not limited to: development of new AI architectures for biology and hybrid models that combine deep
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including electrical/electronic engineering or similar. Experience in federated learning/deep reinforcement learning is preferred. At King’s, you will join a research-leading and multi-disciplinary team led
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hours: 40 | Collective bargaining agreement: §48 VwGr. B1 lit. b (postdoc) Reference no.: 6142 Explore and teach at the University of Vienna, where over 7,500 brilliant minds have found a unique balance
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: 40.00 | Classification CBA: §48 VwGr. B1 lit. b (postdoc) Limited contract until: Job ID: 6142 Explore and teach at the University of Vienna, where over 7,500 brilliant minds have found a unique
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| Working hours: 40.00 | Classification CBA: §48 VwGr. B1 lit. b (postdoc) Limited contract until: Job ID: 6142 Explore and teach at the University of Vienna, where over 7,500 brilliant minds have found a
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/recruitment/2-year-postdoctoral-p… ** Project ** Computational and high field MRI characterization of learning and decision-making ** Supervisor and contact ** Dr Florent MEYNIEL https://www.unicog.org/lab
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Research Center for Molecular Medicine (CeMM), ÖAW | Vienna, Virginia | United States | about 2 months ago
on LazySlide ( et al Nature Methods ), our scalable software foundation, and our deep learning framework for age prediction (Abila et al., Nature Medicine, in press) to engineer a body-scale machine learning
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at: http://dimadamen.github.io/ Prior expertise in audio-visual perception and deep learning methods with a strong publication track record is expected, including first-author publications in CVPR/ICCV/ECCV