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learning and deep learning techniques to the biological sciences. The ideal candidate will have expertise in artificial intelligence, with a specific focus on deep learning applications in structural biology
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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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. 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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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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genetic basis of plant–microbe interactions, with a particular emphasis on data integration across plant species and data types (genomics, transcriptomics). Design, adapt and use deep learning methods
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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 career. Research and technical training Training will include hands-on work in the following areas: Developing and validating deep-learning and machine-learning models using echocardiography
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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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: Proficiency in Python and deep experience with ML/Deep Learning frameworks (e.g., PyTorch, Tensorflow, JAX, HuggingFace). Experience with RL and post-training methods (PPO, GRPO, DPO, reward modeling, RLHF