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- NTNU - Norwegian University of Science and Technology
- Delft University of Technology (TU Delft)
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modeling, computer simulation, non-linear model analysis, interactive learning environments and decision-laboratory experiments. About the project/work tasks: Description of the INTEGRATOR project
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mechanisms that integrate queueing theory, traffic modelling, machine learning, and network-performance prediction for improving latency, reliability and fairness to support mission‑critical services
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. Applicants must have the right to work in the UK. We are unable to offer visa sponsorship for this role. Candidate requirements Candidates must have expertise in developing computer vision and machine learning
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focus on combining simulations using spatial-genetic-demographic individual based models (e.g., using the software SLiM), machine learning approaches, and genomic data to estimate larval dispersal
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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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compositionally complex recycled steels, using density functional theory and machine-learned interatomic potentials, in close collaboration with leading academic partners and Tata Steel. Job description At TU Delft
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, and machine-learning-based analytics. The research work at NTNU will focus particularly on automation, robotics, mechatronic design, sensor integration, and intelligent experimental systems required
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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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for recruitment positions for general criteria for the position. Preferred selection criteria Experience with computer vision, video analysis, self-supervised learning, vision transformers, or multimodal learning
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Machine Learning, Reinforcement Learning, AI-based time-series forecasting English language skills, both written and spoken, corresponding to the scale C1 in the Common European Framework of Reference