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PhD: A deep dive into youth cyberhate Faculty: Faculty of Social and Behavioural Sciences Department: Education & Pedagogy Hours per week: 28 to 40 Application deadline: 5 September 2025 Apply
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tomorrow’s living environments? And how can we support mutual learning between researchers in the geo-information sciences and societal actors when engaging with an increasingly uncertain and turbulent future
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for Sustainable Energy, researchers from academia and industry develop, implement and evaluate new deep reinforcement learning methodology to solve sustainable energy challenges. Key responsibilities The lab is
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models. Requirements The successful applicants will have: A solid computational background, an interest in cognitive neuroscience a and strong deep learning programming skills. Ability to work in an
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of the processing system online. Our approach will be to draw on a broad selection of tools including (deep) reinforcement learning, queuing networks, online algorithms and systems engineering. In addition, a large
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binders that engage with therapeutic targets or efficient (bio)catalysts for synthetic applications. By seamlessly merging cutting-edge directed evolution, next-generation sequencing, and deep learning
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internally, so while you learn the intricacies of our industry, you’ll have plenty of opportunities to contribute and directly affect our bottom line within your first few weeks on the team. While interest in
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simulations with deep learning neural networks and swarm robots, virtual reality experiments, animal communication research, and more. In a range of projects, we show that languages can effectively be seen as
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Organisation Job description Are you passionate about combining the directed evolution of diverse biomolecules with deep learning approaches and contributing to the development of better (bio
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that can be used for training machine learning and deep learning models. You will work in tight collaboration with other researchers in Nijmegen, Delft and at the Hubrecht Institute (van Oudenaarden group