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discipline. Desirable Experience in machine learning, deep learning, data analysis, numerical modelling, or scientific programming (such as Python, MATLAB, or R) is desirable. Knowledge of hydrodynamic
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degree; strong quantitative background and computer skills including programming experience; excellent academic record; and ability to work independently. PREFERRED: Research assistant experience
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Car tires are very important and complex parts with high safety and reliability requirements, responsible for maintaining the contact between a vehicle and the road. Tires are subject to wear due to
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, Computational Neuroscience, Computational Psychology or Behavioural Science; Transport Modelling, Transportation Science or Urban Mobility; Data Science, Artificial Intelligence, Machine Learning
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Position Description The project will focus on the development and application of advanced data-analysis techniques for gravitational-wave science, including machine learning and deep learning
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influencing factors and improve the accuracy, robustness and energy efficiency of intelligent sensing systems. Apply AI as an engineering tool: Use signal processing, statistical methods and machine learning
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experiments and policy-capturing methods may be used to compare interviewer judgements with evidence-based outcome measures. The project will also explore machine-learning, multimodal data analysis, computer
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of video and low-cost sensor technologies to capture subtle movement patterns, creating a rich dataset for AI-driven analysis. Machine learning, deep learning, computer vision and multimodal AI methods will
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how machine-learning-based methods can help overcome this bottleneck, opening the door to excited-state simulations at scales and system sizes that are currently out of reach. You will work at the
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metabolomics, lipidomics, proteomics and genomics, and combine these data using statistical and machine-learning approaches. Established markers such as neurofilament light chain (NfL) and GFAP will provide a