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Institute of Systems and Robotics, Faculty of Sciences and Technology of the University of Coimbra | Portugal | 1 day ago
, aerial, and ground-based systems. Using this data and computational models, the project will simulate various wildfire risk management interventions, spanning from prevention and firefighting to post-fire
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quantum photonics Collaborate with faculty and research staff on the development of theoretical models, experimental design, and data interpretation Prepare and publish high-quality scientific papers and
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background in hydrology, numerical/analytical modeling, programming, and data management/analytics is needed to support existing and forthcoming projects in the BEG hydro group. Purpose We are seeking highly
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Cryptographic Hardware Engineering System-level modelling and simulation Proven publication record in relevant peer-reviewed venues. Excellent analytical, problem-solving, and system-level thinking skills. Strong
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Hardware Engineering System-level modelling and simulation Proven publication record in relevant peer-reviewed venues. Excellent analytical, problem-solving, and system-level thinking skills. Strong verbal
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project to reveal the interaction of galaxies and their cosmic web environment with hydrodynamic simulations based on our new SWIFT code and COLIBRE, a novel state-of-the-art galaxy formation model. You
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of what microscopes can achieve. You will create and apply sophisticated algorithms, physics-based simulations, and machine learning models to process complex data from our cutting-edge imaging systems
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individual to work on an exciting modelling and simulation project, involving extensively applied work at the intersection of software engineering research and modelling and simulation, with real-world testing
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paradigms in alloy science to promote a circular economy. The focus of the Fellowship is to develop deeper metallurgical understanding via characterization and modelling to underpin the design of alloys with
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development of next-generation computational tools for simulating particle-laden two-phase flows by integrating advanced Artificial Intelligence (AI) techniques with traditional computational fluid dynamics