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-constrained machine-learning (ML) models in simulations of turbulent flows. You are expected to contribute to research and development in data-driven methodologies for turbulence modeling in LES (i.e., wall and
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, unlocking reliable perception and navigation where GNSS/GPS cannot be trusted or is unavailable. The project combines ultrasonic sensing, probabilistic perception, and machine learning with advanced robotics
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objectives involving research, code development, supporting software and consulting with scientists. Other relevant qualifications (not required): Bsc in Computer Sciences. Experience with DevOps practices
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biogeochemical modelling and data-driven machine learning approaches at an ecosystem scale to improve our understanding of the fate of nitrogen fertilizers applied to agricultural soils. This understanding will be
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requirements, including link budgets, beam steering, and orbital pointing dynamics. • Experience with optimization methods and physics-informed machine learning. • A strong publication record in antennas
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, that can be documented by a publication record in relevant venues. Solid understanding of state-of-the-art embedded machine learning techniques. Experience in system-level programming, developing prototype
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intelligent control and aerial robotics for navigation in uncertain environment. You will be mainly responsible for implementation of machine-learning algorithms for unmanned aerial vehicles; validation
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have: A relevant PhD degree (e.g., NLP, AI, ML, Security, Cryptography, or a related field) A relevant MSc degree (e.g., Computer Science, Software Engineering, Machine Learning, Artificial Intelligence
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-constrained machine-learning (ML) models in simulations of turbulent flows. You are expected to contribute to research and development in data-driven methodologies for turbulence modeling in LES (i.e., wall and
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. Mathematical skills: Competence in mathematical modeling of dynamic systems and probabilistic frameworks. Experience with machine learning or AI methods for localization or perception (e.g. learning-based SLAM