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print Drucken Do you want to shape the future of AI-driven manufacturing? Are you eager to apply cutting-edge machine learning and optimization techniques to real-world industrial challenges? Join inspire
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the selection of further reactions to run, and their application to targets of medicinal interest. Job description As a PhD student in our growing team, you will develop machine learning methods to predict
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100%, Zurich, fixed-term The Chair of Space Geodesy invites applications for an exciting PhD opportunity focused on advancing research in satellite gravimetry for hydrology using machine learning
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, future projects will also involve patient cohorts. We are currently seeking a motivated and skilled PhD student to join our team. Project background This project focuses on neuromodulatory systems (and the
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of traffic management and control strategies in urban and freeway environments. At the same time, data-driven methodologies and machine learning offer new opportunities for optimal traffic management
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field A strong background and keen interest in uncertainty quantification, structural reliability, optimization, Bayesian inference, surrogate modeling/emulation, and/or machine learning Excellent
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Excellent programming skills (preferably Matlab, or Python), and some experience in machine learning Strong analytical and problem-solving abilities Excellent communication and scientific writing skills, and
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, Information Technology, Information Systems, Statistics, Data Science, Engineering, or a related field Strong machine learning and programming experience with the ability to work across frontend, backend, and embedded
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upcoming areas off the beaten paths. Our three main areas of research are machine learning, distributed systems, and theory of networks. Within these three areas, we are currently working on several projects
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Technical Proficiency: Familiarity with engineering tools with focus on design automation Experience in programming (e.g. Python) or machine learning and a desire to deepen your expertise Experience in