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high-resolution mass spectrometry, in vitro pharmacological characterisation of new psychoactive substances, as well as metabolomics and machine learning. As a PhD student, you devote most of your time
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system integrates robotics, automated sample handling, sensor networks, imaging systems, cloud computing, and machine-learning-based analytics. The research work at NTNU will focus particularly
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on the field can be derived from first principles, and how these constraints can improve the technique's performance, particularly when embedded in modern machine learning models. The ultimate goal is to
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doctoral degree Collect, structure and assess relevant sensor, operational, maintenance, incident and cost data Develop and validate statistical, causal and/or machine-learning methods and turn the results
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, operational, maintenance, incident and cost data Develop and validate statistical, causal and/or machine-learning methods and turn the results into useful decision support Publish and communicate results and
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, micro-CT, particle size analysis, calorimetry, and synchrotron experimental measurement techniques. Knowledge of AI-based and machine-learning methods is also beneficial. For further information about a
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perform both empirical and theoretical work. You will learn how to collect and analyse data within your research area as well as communicate your results at national and international conferences and in
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learning and computer systems. The successful candidate will join an international and collaborative research environment and contribute to advancing efficient AI systems. Are you motivated to take a step
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will then develop XR applications that provide real-time guidance through visual, auditory, or haptic feedback in everyday situations. Using wearable devices, machine learning, and cognitive models you
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systems, or continuous-time and discrete-time LTI systems theory is a plus. Experience with mathematical modeling, optimization, numerical computation, algorithm development, or machine learning. Prior