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equivalency formula. Preferred Qualifications PhD in computer science, engineering, a quantitative discipline, or a domain science with strong computational components. Experience with AI/ML, vision, NLP
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into consideration. We are looking for a candidate with a PhD in Electrical Engineering, Engineering Physics, Mathematics, Computer Science, or a related field, with expertise in programming, physical modeling
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technology Computer science » Modelling tools Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 6 Apr 2026 - 15:00 (Europe/Amsterdam) Country Netherlands Type
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processing, algorithm design, optimisation and simulation, software engineering and automation and control systems. An overview of the current PhD research projects is given here: https://www.dashh.org
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matrix (the “mortar”). Despite its importance, the detailed structure of the interface between these two components remains insufficiently understood. This PhD project aims to deliver a quantitative
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Identification and classification of coherent flow structures in the plasma of the Sun’s photosphere
Identification and classification of coherent flow structures in the plasma of the Sun’s photosphere School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Viktor
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and Civil Engineering is one of eight departments in the Faculty of Engineering. Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/297847/phd-candidate-in-ai-enabl… Requirements
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Faculty of Engineering and Science. One PhD position will be hosted in the Cybersecurity Research Group at the Department of Electronic Systems in Copenhagen, while the other will be hosted in
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» Chemical physics Engineering » Materials engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 6 Apr 2026 - 23:59 (Europe/Paris) Country France Type
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The Laboratory of Energy Science and Engineering (LESE, ETH Zürich) invites applications for a PhD position in the field of CO2 capture, with a focus on structure–performance relationships. At LESE, our research