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4-year PhD fellowship in the Research Programme - Deep Learning-Accelerated Crystallography Pipeline
We welcome applications from candidates with a broad range of academic backgrounds and experiences for a 4-year PhD project on Mathematical and Machine Learning Aspects in Crystallography at Durham
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their 4th or 5th year of studies (M1, M2 or gap year) - Computer vision skills - Machine learning skills (deep learning, perception models, generative AI…) - Python proficiency in a deep learning framework
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on explainable artificial intelligence and intrusion detection for healthcare cybersecurity. The position will contribute to the design, implementation, evaluation, and documentation of machine learning and
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geophysical survey and machine learning algorithms. Mainly responsible for processing geophysical/geomechanical data and analysing the data using machine learning. Conducting research, supervising undergraduate
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and collaborative research at the intersection of quantum information science, machine learning, statistical learning theory, quantum thermodynamics, and complexity science. The Research Fellow will
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Research Scientist leads innovative research initiatives in geospatial science, remote sensing, and GeoAI, applying advanced analytics, machine learning, and image processing techniques to address complex
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machine learning, robotics, embedded and cyber-physical systems, communications and networking, signal processing, semiconductor and VLSI design, quantum computing, and emerging hardware technologies
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transformation, machine learning, and data analysis applied in logistics and supply chain management; support graduate-level teaching activities for both the Supply Chain Management residential (SCMr) and Supply
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the foundations of statistical inference and learning. We are particularly interested in four broad directions. First, we seek candidates in applied statistics, scientific machine learning, or AI for science
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of high-quality GPR data collected at the University of Twente’s Utility Mapping Site (UMS), a unique test environment for utility mapping technologies. Current machine learning models and their training