418 data-"https:"-"https:"-"https:"-"https:"-"University-of-Ostrava" PhD positions in United Kingdom
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systems safer, more efficient, and more sustainable. The aim of this project is to design a smart cognitive navigation framework that information from various sensors and learn to make decisions on its own
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considered for Research Assistant. Strong programming expertise in Python and C++, with experience developing real-time robotic and AI systems. Experience in deep learning and computer vision, including
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pathways, including lifetime extension, targeted component upgrades, partial repowering, full repowering, decommissioning, and asset recycling, drawing on real-world wind farm data and asset management
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PhD Studentship: Bottom-up Decoding of Protein Conformational Landscapes: from Gas-phase to Solution
folding simulations with mass spectrometry experimental data, creating a unique multi-stranded methodology to map out free energy landscapes associated with protein folding in environments spanning gas
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they can reliably, affordably, and fairly support a net-zero energy system. The research will focus on how data-driven and machine-learning-based control can coordinate demand, storage, and local generation
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perform outside the lab, how they are perceived and accepted by users, and how data from real-world deployments can inform the design of personalised support strategies. You will work closely with engineers
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master’s degree in economics, business, or any social science with the following: • Subject knowledge (economics, data science, agriculture, environmental science, or social science, structural equation
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in a relevant subject – Biomedical Sciences, Biomedical/Information Engineering, Computer Science, Analytical Bioscience, Physics or related disciplines. Prior experience with medical imaging
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professional development workshops in areas such as research communication, computing and data science, and professional progression through our Early Career Researcher Institute .Additionally, you will be
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4D imaging, their high data rates limit their use in real-time applications. As a result, the available benchmark data for developing numerical models to predict transient multiphase flows is limited