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Field
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This PhD project, part of the REACT MSCA Doctoral Network, aims to develop an energy-efficient compute-in-memory (CIM) architecture using gain-cell memory for real-time edge learning, addressing
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
(ph.d.) in artistic development work at the Norwegian University of Science and Technology (NTNU) for general criteria for the position. Preferred selection criteria Experience with machine learning
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on information theory, machine learning, and control to analyse how local variability, sensor drift, and platform differences affect both global model performance and human supervisory factors such as workload and
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-Physical Energy Systems The PhD position focuses on the development of secure and trustworthy AI for resource-constrained embedded systems. The research will investigate how machine learning models can be
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15th October 2026 Languages English English English The Department of Energy and Process Engineering has a vacancy for a PhD Candidate in Integrated Energy Systems for Sustainable Digital
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projects involve large-scale population cohorts, single-cell genomics, statistical genetics, functional genomics, machine learning, and clinical translation. We are a diverse and international team
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and machine-learning methods for multi-objective optimization of efficiency, reproducibility, and operational stability Study intrinsic material stability, light-induced phase segregation, ion migration
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motivated students with a strong background in engineering or computer science. The ideal candidate will have: Strong programming and software skills. An awareness of machine learning theory and techniques
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should ideally have experience in: Essential Deep learning and machine learning Computer vision Python programming PyTorch or TensorFlow Strong mathematical and analytical skills Desirable Video
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modeling, geometric deep learning or physics-informed machine learning, or you are willing to learn these quickly; strong collaboration skills: you enjoy working in a multidisciplinary team and feel