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Field
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involves developing state-of-the-art methods for image segmentation, detection, classification, predictive modelling, and image enhancement. We aim to build more trustworthy and robust AI models that can
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solutions using first motion. Experience in full waveform inversion using innovative tools (e.g. ISOLA) and methods (ML), earthquake location algorithms, computer programming and geophysical equipment
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algorithms (such as machine learning or clustering). Familiarize with the multi-level data and how to model them in a polystore architecture [2] or similar (Month 1 – 12). Develop an environment to test the
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experimental systems for cryogenic measurements Development of a microwave quantum control & readout stack Development of Python code to operate quantum systems Detailed experimental characterization
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for expensive new dispatchable generation capacity, enable deeper renewable energy penetration, mitigate grid congestion, and reducing CO₂ emissions at national scale. In this PhD project, you will develop
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within the Norwegian Center on AI for Decision (aiD), benefiting from broad and divers expertise, and strong industrial connections. The project will have theoretical and algorithmic developments, software
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. The project will have theoretical and algorithmic developments, software developments, and industrial case studies. Duties of the position Complete the doctoral education until obtaining a doctorate Carry out
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methods that reduce compute, energy usage, memory and storage demands, and associated carbon emissions while aiming to maintain model quality. Your work will include developing new methodologies and
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terms of safety and learnability Develop algorithms to detect and characterize harmful market outcomes Design and test market rules that steer multi-agent learning dynamics Combine theoretical analysis
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will develop and evaluate fault detection and fault location algorithms for these systems. The project is funded by GE Vernova under a wider collaboration with Imperial College London. You will be co