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decision‐making skills. Demonstrated superior planning, budgeting and project management skills. Strong computer skills with advanced proficiency in Microsoft Word and Excel. Experience with FIS, HRIS, and
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architectures for foundation models The work will combine methodological development with large-scale experiments, aiming for contributions at leading machine learning and computer vision venues such as NeurIPS
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. Master’s or PhD in Electrical Engineering, Computer Science, or related discipline. Research experience in machine learning, power systems, or cybersecurity. Proficiency in Python programming. Familiarity
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, you will have as a minimum: A PhD, or evidence of imminent PhD completion, in quantum information, physics, mathematics, computer science, or a closely related discipline. Demonstrated research
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Generation, digital double, human-like avatars. Profile PhD in Computer Vision Background in CS Research experience in gen AI, deep learning Strong computational and analytical skills, as well as experience
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management, particularly in the establishment, operation, and further development of research infrastructures, as well as with outstanding research expertise in manual and computer-assisted content analysis
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demands. Teamwork: Ability to work collaboratively with others and contribute to a team environment. Technical Proficiency: Skilled in using office software, technology, and relevant computer applications
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assessments, accident investigations, and emergency action plans. • Familiarity with equipment and instrument maintenance, repairs, and replacement • Proficiency with basic computer software such as Microsoft
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to maintain building and computer inventory for CMES space at 38 Kirkland and 17 Sumner. Serve as Room Scheduler, coordinating and allocating CMES conference room space (rooms 102 and 208). Serve as Phone
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for the scaling phase of the programme (2030-2035). The programme has a strong focus on materials R&D for realizing state of the art quantum processor technologies, which will be synthesized in ultra-high vacuum