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high-risk/high-gain research without any teaching obligations. The position is funded through the support of Google.org (more details here ). Our group performs fundamental and applied research in many
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al. 2019] and point-force Lagrangian models, with advanced post-processings [Vegad2024]. This work will be carried out with the YALES2 high-performance platform. Where to apply Website https
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and an inviting sports programme at discounted rates. Selection process Please send your application (cover letter, resume, academic transcripts, high school diploma and the contact details of exactly
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, molecular dynamics, and the use of high-performance computing is advantageous. Also, experience from synchrotron or neutron facilities is an advantage but not a requirement. Excellent oral and written
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of this PhD project is to develop machine learning algorithms that perform efficiently and coherently across both classical and quantum computing platforms. The PhD project falls under the collaboration between
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University of Veterinary Medicine Hannover • | Hannover, Niedersachsen | Germany | about 6 hours ago
during seminars or by participating in doctoral colloquia. Each student's performance in the required courses of the PhD programme will be evaluated by regular testing and confirmed by the supervisor. PhD
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employed to achieve an appropriate trade-off between reliability, performance, computational efficiency, and adaptability under uncertainty. The candidate will be affiliated with CRADLE (Center for Robotic
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specialized through efficient fine-tuning and distillation to improve throughput in terms of both compute and data acquisition, while maintaining guaranteed performance on critical failure modes. This PhD
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the wind/renewable energy training and research elements of the CDT programme. Funded by Siemens Energy and EPSRC, this 4 year this PhD studentship, at the University of Strathclyde is in the area of
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have the opportunity to exploit some of the cutting-edge experimental and computational methods, comprising constraint-based and kinetic modeling, statistical analysis of large datasets, high-throughput