616 machine-learning "https:" "https:" "https:" "https:" "https:" "Dana Farber Cancer Institute" positions in Norway
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hazards, enhancing asset protection, maritime security, emergency preparedness, and societal resilience. The project will leverage advanced AI and machine learning techniques to enable predictive risk
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Digital. The research focuses on advanced signal analysis and machine learning methods that enable robust operation and service continuity in future wireless networks under challenging radio conditions. As
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complementary and synergic methods at the intersection of Artificial intelligence, Machine learning, Numerical simulation, Formal verification. Such methods include, among the others: AI-guided simulation
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outside of EU/ EEA countries and exemptions from the requirements: https://www.mn.uio.no/english/research/phd/regulations/regulations.html#toc8 Desirable experience (in ranked order): • Research in
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positions are closely linked, and candidates will have the opportunity to collaborate together and learn methods from the parallel project, building a broad skillset. Both positions are part of the larger
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to SAFE. Delivering EVU course from SAFE center. Required selection criteria A PhD degree (or equivalent) in biometrics, information security, computer science, electrical engineering, or machine learning
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» Autonomic computing Engineering » Maritime engineering Technology » Computer technology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 25 Apr 2026 - 23:59 (Europe
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95 94 31 Deadline for applications is 5 May 2026. This date also applies to the submission of publications/written work. Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/298248
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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
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. Strong (inter-)national network in field of application. Experience with high-performance computing (HPC) and large datasets. Experience with machine learning applied to geophysical signals. Experience in