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feedback control in microbial fermentation. Typical tasks include: AI for spectroscopy analytics: spectral pre-processing; chemometrics and ML (PLS baseline; modern ML/deep learning as appropriate); drift
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Experience with deep learning for image analysis and/or medical image processing Knowledge of self-supervised learning, representation learning, and/or generative models Experience with multimodal machine
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25th February 2026 Languages English English English The Department of Materials Science and Engineering has a vacancy for a PhD Candidate in machine learning and large language models (LLMs
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investment in artificial intelligence over the next five years (regjeringen.no ) (in Norwegian). About the project/work tasks: This PhD fellowship is associated with the third cluster of the AI LEARN centre
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the broader framework of Embodied AI. The goal is to integrate physical models with deep learning to create interpretable, data-driven observers that enable physically grounded perception and control for robust
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selection criteria Solid theoretical background in robot perception and navigation. Deep foundation in modern machine learning. Solid programming skills in C++ and Python. Experience with ROS is a plus
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for the position. Preferred selection criteria Solid theoretical background in robot perception and navigation. Deep foundation in modern machine learning. Solid programming skills in C++ and Python. Experience with
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Language Model-based application development. Knowledge Graph Development for Sensor Data. Deep Learning techniques, Data Engineering, and Semantic Technologies Open-source artificial intelligence, machine
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skills in relevant programming languages, particularly python Knowledge of, and experience from, the maritime industry Knowledge of generative models, reinforcement learning, or geometric deep learning