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Field
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selection criteria Knowledge of sensors and measurement techniques Experience from the industry providing sails for merchant ships Experience with Computational Fluid Mechanics Knowledge of machine learning
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paradigms that support collaborative processes rather than isolated individual use. Combining perspectives from the learning sciences, Computer-Supported Collaborative Learning (CSCL), Computer-Supported
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machine learning-driven digital twins for predictive combustion modeling. The research program will cover a wide range of e-fuels (H₂, NH₃, CH₃OH, DME, OME) and their applications in industrial furnaces
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machine-learning approaches. Established markers such as neurofilament light chain (NfL) and GFAP will provide a biological reference point for identifying disease-specific biomarkers. A central part of the
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applicants with a background or experience in: • Computational chemistry • Materials simulation • Scientific machine learning / AI • Molecular dynamics or DFT • Materials science or a related discipline
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://www.academictransfer.com/en/jobs/363733/phd-candidate-multimodal-and-ag… Requirements Specific Requirements Profile We welcome candidates with an MSc or equivalent expertise in artificial intelligence, medicine, computer or
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multimodal foundation architectures. Full-time, TV-L E13, 100%, fixed-term for 3+ years. Position PhD Student (100%) Remuneration TV-L E13 (Fully Funded, fixed-term for 3+ years) Field Representation Learning
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and hardware security assurance for embedded systems by combining advanced side-channel analysis, fault-injection techniques, AI- and machine-learning-assisted analysis, robustness evaluation, and
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appointment. Job duties Mentoring new PhD students in numerical computational projects. Conducting comprehensive literature reviews in machine learning for subsurface flow and well performance and supporting
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with European industry. The EISLAB division at Luleå University of Technology conducts research in electronic systems design, sensor systems, cyber-physical systems, the Internet of Things and machine learning