202 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" Postdoctoral research jobs in Denmark
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Application deadline December 1, 2025, at 23:59 CET Apply online https://fa-eosd-saasfaprod1.fa.ocs.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1001/jobs/preview/3144
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(written and spoken). Conduct independent research. Have high self-motivation to learn. Be able to work well and communicate expert knowledge in an interdisciplinary team. Have a strong sense of
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capabilities for batch analysis, visualization, and management of high-throughput experimental data based on the open-source NOMAD database model (https://nomad-lab.eu/nomad-lab/ ) Regularly and proactively meet
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analysis to translate THz signals into optical material properties such as refractive index and absorption coefficient. Development of machine learning algorithms for material classification. Exploration
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(e.g. using COBRApy or related toolboxes), or a strong motivation to develop this expertise. Data science, AI/ML, and digital surrogate models Experience with data science and machine learning, including
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analysis to translate THz signals into optical material properties such as refractive index and absorption coefficient. Development of machine learning algorithms for material classification. Exploration
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management ”, funded by the EU Commission’s Horizon Europe Framework. The successful candidate will work on two integrated research agendas: Explore traditional and machine-learning based techniques in
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Research Focus We are offering a Postdoctoral position in graph machine learning, algorithms, and graph management with particular focus on: Modeling real-world spatio-temporal energy networks Developing
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, unlocking reliable perception and navigation where GNSS/GPS cannot be trusted or is unavailable. The project combines ultrasonic sensing, probabilistic perception, and machine learning with advanced robotics
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-constrained machine-learning (ML) models in simulations of turbulent flows. You are expected to contribute to research and development in data-driven methodologies for turbulence modeling in LES (i.e., wall and