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- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
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such as mechanistic, chemometric, deep learning, and physics-aware models. Improve robustness and reliability of the developed methods for deploying AI models in real environments utilizing augmentation
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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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data-driven learning and which should remain within structured optimization. In line with AID’s research areas, the project will emphasize knowledge embedding, uncertainty representation, risk-aware
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criteria Machine Learning Expertise: A robust foundation in probabilistic modeling, Bayesian inference, deep learning, and/or anomaly detection Modeling & Simulation Experience: Familiarity with Building
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, structure preserving deep learning, stochastic differential equations, generative AI, numerical optimization. Strong programming skills (Python, Julia, Jax). Experience with numerical optimization is also
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implementations and hardware-security countermeasures. Experience with hardware reverse engineering, debugging interfaces, or firmware analysis. Experience with machine learning, deep learning, signal processing
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for changes in your work tasks after appointment Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/307999/phd-research-fellow-in-gr… Requirements Research FieldTechnology » Environmental
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Is the Job related to staff position within a Research Infrastructure? No Offer Description The Hybrid Technology Hub CoE (https://www.med.uio.no/hth/english/ ) is inviting applications for a three
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SINTEF Ocean; FME Martrans https://martrans.no/ and the Norwegian Maritime AI Centre https://www.ntnu.edu/mai The research is foreseen to focus on fluid-dynamic properties of the different sail types
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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