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machine learning for cybersecurity, current systems remain largely based on pattern recognition and struggle to incorporate contextual reasoning, temporal dependencies, and relationships between entities
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employment. Starting date: 09.04.2026 Job description:PhD Position: Deep learning for phase-contrast synchrotron X-ray tomography Reference code: 987 - 2026/WP 1 Work location: Hamburg Application deadline
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Foothill-De Anza Community College District | Los Altos, California | United States | about 2 months ago
/Administrative Internal Number: 6978145 PT Learning Disabilities Specialist Pool PART-TIME LEARNING DISABILITIES INSTRUCTOR Foothill-De Anza Community College District is establishing a pool of qualified
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EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD Position: Deep learning for phase-contrast synchrotron X-ray tomography Reference code: 2026
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Previous Job Job Title Temp/Casual Learning Assistant Next Job Apply for Job Job ID 372125 Location Twin Cities Job Family Supplemental Employee Full/Part Time Part-Time Regular/Temporary Regular
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 14 hours ago
Associate Dean for Simulation and Experiential Learning Appointment Type Open Rank Vacancy ID FAC0005862 Full-time/Part-time Full-Time Permanent Hours per week 40 FTE 1 Position Location North Carolina, US
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systems, statistical physics and machine learning, and using these insights to develop new methods, with the support of competent and friendly colleagues in an international environment? Are you looking
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learning, discovery and personal growth. The University is accredited by the Southern Association of Colleges and Schools and has earned special accreditation from professional and academic associations
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to creative, healthy, and nutritious foods, international cuisines, and sustainability throughout all dining operations. Position Summary: The Learning and Development Lead at Michigan Dining is responsible
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Overview This project proposes a novel quantum-enhanced learning analytics framework for higher education, focusing on early identification of at-risk students and optimisation of intervention