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:10.1093/nar/gkaf1388), we will develop machine learning tools to model microbial communities and their impact. The environment: The successful applicant will work within the Hildebrand and Traka groups
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physics-informed machine-learning models for binding affinity predictions in rational small-molecule drug design. The models will allow prioritisation of candidates from hit discovery through to lead
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) (required) - Quantum libraries (Cirq, Qiskit, Tensorflow Quantum. QuTiP) (recommended) Theory: - Machine Learning, Deep Learning, Reinforcement Learning, Data Science (required) - Quantum Computing, Quantum
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and assist with data analysis, including mathematical modeling and/or machine learning: keep accurate records of experiments and results perform data interpretation/summarization including writing
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analyses. Machine learning for biological data (e.g., protein language models, transformers, generative models) and interest in building interpretable tools for experimental colleagues. Qualifications PhD
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assimilation, machine learning, and seasonal weather forecasts. As a Postdoctoral Research Fellow, you will play a crucial role in developing and testing statistical models for the accurate forecasting
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Architecture Search (NAS) that can automatically design efficient deep learning models optimized for specific embedded hardware platforms. These models will be deployed in resource-constrained, standalone
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for showcasing the improved mapping and monitoring of forest traits and uncertainties. You will be mainly in charge of: Develop improved hybrid model inversion methods with a focus on machine learning and deep
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practical applications, including solving mathematical reasoning problems. The ideal candidate has a strong background in machine learning and an interest in bridging rigorous theoretical insights with
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of next-generation machine learning models applied to the analysis of multichannel temporal signals, with a special focus on sleep medicine. This project will utilize polysomnographic recording databases