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learning will be explored as enabling technologies for automated leakage detection and localization, analysis of complex measurements, fault-response characterization, intelligent exploration of large
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operational resilience. Your immediate manager will be the Head of Discipline. About the project Maritime operations increasingly depend on interconnected operational technology, automation, remote access and
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resilience through automated and scalable cyber range exercises. The project provides flexibility for the successful candidate to shape the research direction within the broader topic Research Objectives and
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to qualify for security clearance at level CONFIDENTIAL or higher. https://lovdata.no/dokument/NLE/lov/2018-06-01-24/KAPITTEL_8#KAPITTEL_8 The appointment is to be made in accordance with NTNUs guidelines
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team includes: Prof. Ivan Depina – main supervisor and coordinator, probabilistic modelling, scientific machine learning Prof. Mohamed Hamdy – building performance simulation, building automation
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methods. Knowledge of bioinformatics, DNA-sequence analysis or computational design tools. Experience with high-throughput experimental workflows, automation or design–build–test–learn cycles. Experience
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education. You must meet the requirements for admission to the faculty's doctoral program (see: https://www.ntnu.edu/ie/research/phd ). You must meet the requirements for admission to the IE Faculty. PLEASE
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aims to bring automated, cost-effective damage detection to port structures that have historically lacked any formal monitoring. The research is anchored at the Magerholm Research Quay (MRQ), a unique
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workflows under computational constraints. Rather than automating predefined pipelines, the agents act as meta-decision-makers, reasoning about modelling assumptions, approximation levels, and the choice
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computational constraints. Rather than automating predefined pipelines, the agents act as meta-decision-makers, reasoning about modelling assumptions, approximation levels, and the choice of risk measures