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Learning, Natural Language Processing, Human-Computer Interaction, Digital Health, Endocrinology Secondments (Preliminary Plan): UiB (Norway): 1–2 months — Patient and caregiver interviews, exploration
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. Research Fields: Artificial Intelligence, Multimodal Machine Learning, Natural Language Processing, Human-Computer Interaction, Digital Health, Endocrinology Secondments (Preliminary Plan): UiB (Norway): 1–2
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Deadline 28 Feb 2026 - 23:00 (UTC) Type of Contract To be defined Job Status Full-time Hours Per Week To be defined Is the job funded through the EU Research Framework Programme? Not funded by a EU programme
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experience with CAD/FEM software Experience in one or more of the following fields is a plus: simulation frameworks (e.g. SOFA, NVIDIA IsaacLab), ROS1/2, machine learning, computer vision Excellent
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sizes and frequencies by: Measuring rock fractures from UAV data using manual and automated mapping approaches (e.g., machine learning, convolutional neural networks). Monitoring physical weathering
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stimulating, interdisciplinary environment. develop and validate machine learning models to extract digital biomarkers for atypical parkinsonism from real-world wearable sensor data. interpret findings in close
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Metal additive manufacturing process monitoring and control – Researcher, PhD position (ERC project)
of process condition variations. The important parts of the control system to be developed within this project are i) coaxial measuring of meltpool depth variations, and ii) machine learning-based models
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. Until now, specific EN fingerprints of localized corrosion are determined manually. This is a tedious procedure that requires considerable expert knowledge. Artificial intelligence or machine learning (AI
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(PSI), within the research group EAVISE. The project explores audio representation learning for low-resource settings. Recent advances in machine learning for audio have focused on learning
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the application of machine learning and artificial intelligence. By using neural networks developed in Python, the project aims to generate robust and generalisable models for scaffold design. Industrial