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, you will conduct cutting-edge research in these areas. You will learn state-of-the-art techniques in formal methods and knowledge representation and apply them to high-impact use cases related
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related to models and multiple sources of data describing ecological dynamics. The PhD project will address the following aims: 1) Develop efficient tools for learning about models from data, 2
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to machine learning. This PhD provides a unique opportunity to shape emerging concepts in Artificial Intelligence Informed Mechanics (AIIM), combining fundamental research with methodological innovation. You
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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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Environment Convergence Environment. Clim-SHOCK investigates volcanic climate shocks from the past and places them into a future scenario. What can we learn from the past to improve future climate projections
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) to enhance AML capabilities. AI-driven solutions can learn from vast datasets to spot hidden patterns and anomalies beyond human or rule-based detection. For more information and how to apply: https
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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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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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. The project is conducted in close collaboration with the SFF Integreat, The Norwegian Centre for Knowledge-driven Machine Learning (ML) , a centre of excellence funded by RCN and in operation until 2033
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this by concentrating on five select research areas in ICT. Learn more about: working at Simula and careers at Simula Project/Job description In the Department of ComplexSE, we are now offering a