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1st October 2026 Languages English English English The Department of Electronic Systems has a vacancy for a PhD Candidate in Machine Learning & Signal Processing for Industrial Applications Apply
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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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, research institutes, industry, public agencies, and leading global institutions. We welcome motivated applicants in robotics, control, AI, machine learning, physics, and related fields, including early-stage
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, control, AI, machine learning, physics, and related fields, including early-stage researchers eager to contribute to this emerging scientific frontier. About the project The role of the PhD candidate will
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engineering disciplines, including structural mechanics, hydrodynamics and machine learning Strong programming skills in Python and/or MATLAB Experience with scientific computing, CFD/FEM software, potential
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meet the requirements for admission to the faculty's doctoral programme in Engineering Cybernetics . Strong programming skills, in particular Python, and practical experience with modern machine learning
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
for the position. Preferred selection criteria Experience with machine learning or other relevant AI technologies Scandinavian language skills Previous experience from industry or research in engineer-to-order
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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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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position We have a vacancy for a PhD candidate in machine learning
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focus on combining simulations using spatial-genetic-demographic individual based models (e.g., using the software SLiM), machine learning approaches, and genomic data to estimate larval dispersal