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south of Oslo. via Unsplash Main responsibilities Development and testing of digital solutions for small forest owners. These solutions include Computer vision models and a digital assistant based on a
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of physics-informed machine learning proxy models for large-scale CO₂ storage. The project addresses the significant computational burden of numerical simulation and optimisation of large-scale CO₂ storage
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degree. Required qualifications (documentation required) Academic background as listed above Training in machine learning Very good English skills (written and oral) Very good computer programming skills
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synthetic biology, machine learning (ML), and ultrahigh-throughput screening (microfluidics) to discover new enzymes and bioactive molecules with applications in biotechnology, medicine, and sustainability
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Technology has a vacancy for 1 PhD Research Fellow in Privacy Preserving Machine Learning. The successful candidate will be offered a 3-year position. Are you motivated to take a step towards a doctorate and
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Academically relevant background within marine control/cybernetics, computer science, or hydrodynamics, with good skills in mathematics, programming, and machine learning. Master's degree in control engineering
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Academically relevant background within marine control/cybernetics, computer science, or hydrodynamics, with good skills in mathematics, programming, and machine learning. Master's degree in control engineering
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of Information Security and Communication Technology has a vacancy for 1 PhD Research Fellow in Privacy Preserving Machine Learning. The successful candidate will be offered a 3-year position. Are you motivated
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Cybernetics at NTNU is offering a fully funded PhD position in the area of learning-based control and decision-making for complex multi-agent systems. The project explores new computational frameworks
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to your work duties after employment. Required selection criteria You must have a relevant Master's degree in Computer Science, Artificial Intelligence, Data Sciecnce (with a focus on machine learning