156 machine-learning "https:" "https:" "https:" "https:" "https:" "University of St" "St" "St" PhD scholarships in Norway
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Computer science » Computer systems Computer science » Programming Technology » Communication technology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 26 Apr 2026 - 23
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invites applications for a PhD position focused on developing a theoretical framework for monitoring and updating adaptive learning systems (including machine learning/artificial intelligence systems) under
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universities, research institutes, industry, public agencies, and leading global institutions. We welcome motivated applicants in robotics, control, AI, machine learning, physics, and related fields, including
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areas: Developing and training robust machine learning surrogates to replace computationally expensive high-fidelity simulations, enabling exploration of vast design spaces. Formulating optimization
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interests through elective courses and secondments. • Blended Learning Approach: Our training combines intensive in-person workshops at partner institutions with regular interactive online seminars, journal
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with the Civil Servant Act, the Security Act, and the Export Control Act. Interested in learning more about the position? We are happy to tell you more about life on campus. Contact associate professor
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of experts? Then this job may be for you. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/296128/phd-fellowship-in-systems-neuroscience Where to apply Website https
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Experience with AI / probabilistic AI / Machine Learning Experience with numerical optimization and MPC Strong programming skills (Python, C) Experience with predictive maintenance, fatigue, fault detection
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complex biological systems. Research Environment & Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable
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into reliable information about structural and aerodynamic behaviour remains a challenge. The PhD will develop data-driven methods that combine measurements, physics-based models, and machine learning to extract