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: Framework Development: Design and implement a generative deep learning framework for cross-modal integration and analysis, resilient to distribution shifts. Correlation Discovery: Identify interpretable
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platform. Training the development digital-twin using real-time data from hardware available Electrical power level studies with developed digital twin to identify visible solutions for distribution electric
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explore unconventional ideas, develop computer algorithms for data analysis, create new experimental approaches, and apply the technique in areas like biomedicine, materials science, and geology. My group
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algorithms, validated navigation architectures, and new insights into next-generation intelligent mobility solutions. The student will undertake two industry placements at Spirent, use high-tech simulation
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aims to advance the field of time and frequency (TF) transmission in data communication networks. The focus of the research will be on distributed fiber optic sensing (FDOS) and machine learning
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on developing machine learning algorithms to support the use of complex urban simulators in decision-making under uncertainty. This PhD project shifts the focus from optimality to relevance in urban land-use and
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for distribution electric propulsion. Who we are looking for We are looking for enthusiastic, self-motivated applicants with first-class degree in Electrical Engineering, Aerospace Engineering or Computer Science
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involves the use of quantum chemistry, machine learning, and genetic algorithms to search for new homogeneous chemical catalysts. Who are we looking for? We are looking for candidates within the field
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Job Description We invite applications for a fully funded PhD position focused on the development of advanced computer vision and machine learning algorithms for detection and identification
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with industry partners. Excellent communication skills and experience contributing to software development within distributed, multicultural teams. Proficiency in German is required (B1 certificate