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of novel physics-guided AI algorithms for drug design, integrating physics-based modeling with state-of-the-art deep learning methods. The project will focus on creating a next-generation docking framework
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Your position New computational and analytical tools in population genetics allow to estimate both contemporary and past effective population sizes. With these in hands, the project aims to verify
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flow reconstruction, enabling both real-time coarse diagnostics and high-fidelity offline velocity field estimation. Developing reinforcement learning (RL) algorithms for a multi-agent robotics system
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) in Basel, Switzerland. The successful candidate will work on developing cellular therapies against glioblastoma and brain metastasis. The project focus is on the genetic modification of T cells and
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. The PhD position will focus on developing a deep-learning algorithm for analyzing the acquired experimental data. The PhD position will focus on development a comprehensive and AI-driven platform
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to demonstrate real-world feasibility. The overarching goal is to bridge high-level algorithmic innovation with energy-aware hardware deployment, enabling intelligent sensor systems that act as autonomous micro
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reinforcement learning for large language models (LLMs). Research directions include developing next-generation post-training algorithms, exploring diffusion-based approaches to reasoning with language models
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position (technician) will focus on performing Raman/FTIR on retrieved samples. The PhD position will focus on developing a deep-learning algorithm for analyzing the acquired experimental data.
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matter physics, and quantum computing. You will dive deep into the realms of theoretical and computational physics with a focus on the development of algorithms and large-scale numerical simulations. Your
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and the underlying genetic networks through comparative transcriptomics analyses. The work will involve fieldwork in East Africa and laboratory research in Switzerland, with additional visits