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7 Aug 2026 Job Information Organisation/Company Sofia University "St. Kliment Ohridski" Department FEBA Research Field Computer science Engineering » Computer engineering Information
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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position As agile development scales to larger, more complex organizations
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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position Are you motivated to take a step towards a doctorate and open
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | about 1 month ago
: Good programming skills, preferably in Python, and strong analytical abilities. A solid background in machine learning, computer security, privacy, distributed systems, or a closely related area
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perform both empirical and theoretical work. You will learn how to collect and analyse data within your research area as well as communicate your results at national and international conferences and in
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), Computer Science (Machine learning, Efficient Algorithms and High Performance Computing), and Physics (Image Formation Modelling). Your project is part of the DUAL-IMPACT project, which focuses on the development
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University of Science and Technology (NTNU) has a vacant position as PhD candidate in the field of machine learning for materials science. Your immediate leader will be the Head of Department. About the
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, machine learning, or NLP Published work in reputable conferences or journals Outstanding academic performance in relevant modules or degrees A strong motivation to work on cutting-edge research in Agentic
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SINTEF, Equinor, and Total. The main objectives of the project include the development and the integration of signal processing and machine learning methodologies aiming to improve flow assurance via field
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and hardware security assurance for embedded systems by combining advanced side-channel analysis, fault-injection techniques, AI- and machine-learning-assisted analysis, robustness evaluation, and