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Field
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for candidates with: A strong BSc and MSc degree in computer science, machine learning, mathematics, physics, engineering, or a related field Strong foundations in machine learning and deep learning Experience
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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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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
(ph.d.) in artistic development work at the Norwegian University of Science and Technology (NTNU) for general criteria for the position. Preferred selection criteria Experience with machine learning
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of seven departments in the Faculty of Information Technology and Electrical Engineering . Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/307240/phd-candidate-in-machine-… Requirements
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skills. Fluent in English, both spoken and written. Willing to learn the Dutch language. TU Delft (Delft University of Technology) Working at TU Delft means contributing to solutions that really make a
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% of your total appointment, averaged over the course of your PhD. Job requirements Essential An MSc degree in systems & control, electrical engineering, mechanical engineering, applied mathematics, physics
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% of your time), including tutorials and supervision of Bachelor’s theses. This is what we ask of you This is an interdisciplinary project that combines machine learning and AI, probabilistic risk
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-convex optimisation, performance analysis, machine learning, etc.) 1) Applicants should have, or expect to achieve, at least a master’s (or international equivalent) in a relevant science or engineering
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try again. UiO/Anders Lien 4th October 2026 Languages English English English 3-years PhD position in probabilistic machine learning and statistics Apply for this job See advertisement About the
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sensors capable of characterizing the dynamic state of buildings and their inhabitants. By combining multi-microphone acquisition techniques with state-of-the-art machine learning methods, acoustic sensing