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collaborators. Qualifications Applicants must hold a PhD degree in electrical/electronics engineering, telecommunications or related field. Other requirements include: Strong background in communication theory
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machine learning technologies in order to provide evidence-based decision support tools in near real time across a variety of thematic domains: disaster risk reduction, sustainable agri-food systems
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descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange isotherm parameters directly from molecular properties. These predictions will be integrated
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data
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expertise in the RTG-addressed PhD subjects, high interdisciplinary desire to learn and willingness to cooperate, very good verbal and written English communication skills as well as the absolute
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requirements: Master’s degree or equivalent preferably in machine learning or equivalent fields. A Master’s degree in computer science/statistics/applied mathematics/electrical engineering can also be considered
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skills and experience: Essential criteria PhD or equivalent (or thesis submitted*) in at least one of the following subjects: Computer Science, Machine Learning, Biomedical Engineering, Medical Imaging
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to contribute to a project at the intersection of biotechnology, drug development, and computational analysis. Candidate will be involved in: Collaborating with computer scientists and engineers to develop AI
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engineering Engineering » Electrical engineering Researcher Profile First Stage Researcher (R1) Application Deadline 14 Feb 2026 - 22:59 (UTC) Country Netherlands Type of Contract Temporary Job Status Not
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, and training methods - across multiple technological platforms - photonics, electronics, biological neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning