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- “HORIA HULUBEI” National Institute for Research and Development in Physics and Nuclear Engineering
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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maintain existing analysis/sorting equipment and computer systems. Participate in the selection of new facility equipment and software with advancing technology so that the facility maintains its status as a
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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teaching duties. Requirements: Applicants should possess a PhD degree in Computer Science, Computer Engineering, Information Systems, or a related field, and sufficiently demonstrate abilities to conduct
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“HORIA HULUBEI” National Institute for Research and Development in Physics and Nuclear Engineering | Romania | about 2 months ago
/ Medical Physics • Currently enrolled for PhD studies (proof of enrolment and endorsement from PhD advisor required) • Computer operating/programming skills: Microsoft Office Suite, MATLAB, Origin or other
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Machine Learning; (ii) Big Data and Data Management; (iii) Computer Vision and Pattern Recognition; and (iv) Distributed Systems and Networking. These key research areas have a special thematic focus on (a
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optics, optics simulation and design. We are pleased to advertise a Doctoral Researcher (PhD) position to begin in Octiber 2026, as part of the Doctoral Network of the European Commission HIPOVor “High
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | about 1 month ago
profile # Ph.D. in the field of Physics, Chemistry, Computer Sciences or Applied Mathematics or related field # Background in mathematical surrogate modeling and machine learning # Experience or familiarity
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and energy-efficient manufacturing in the Greater Region. Modern industrial production systems rely on networks of sensors to monitor processes, energy consumption, material flows, and environmental
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currently lack reliable uncertainty estimates, limiting error detection and automation. The UMLFF project aims to develop next-generation MLFFs with built-in uncertainty predictions to enable safe, automated