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. This project will develop and apply new computational/analytical tools to guide XFEL experiments for specifically tracking lattice fluctuations and ion dynamics in energy materials (batteries). The project will
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Applications are invited for a position in the rapidly expanding data analytics run by Prof Adam Dubis. The main focus of the team is to develop deep learning tools for prediction of disease progression
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and testing behavior change interventions as an advantage. Strong analytical, problem-solving, and organizational skills, with the ability to work both independently and collaboratively. Initiative
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, secure digital communication, medical technology, and new analytical methods. We use and develop basic theory, next-generation instrumentation, and software, and aim to make our data publicly accessible
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analytical frameworks grounded in Mean Field Game (MFG) theory and Multi-Agent Reinforcement Learning (MARL), which are tailored for eCPS. These frameworks will facilitate the creation of effective control
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from conventional materials (e.g., traditional metals), two-dimensional materials, and their combinations. The project will employ a broad spectrum of analytical, semi-analytical, and numerical
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for candidates who have: Strong programming skills, particularly in Python Solid analytical and mathematical abilities Experience with machine learning Strong communication skills and proficiency in English The
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. As part of the SPP, the SoilEnergySpots project is jointly led by Prof. Dr. Nicole Strittmatter (Analytical Chemistry), Prof. Dr. Mirjana Minceva (Biothermodynamics), and Dr. Steffen Schweizer (Soil
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analytical techniques. Strong interpersonal and teamwork skills are required to support collaboration both within DTU National Food Institute and with external partners. Flexibility and self-motivation
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. Understanding of or curiosity about machine learning, AI, or cloud computing tools used in agricultural analytics. Interest or experience in working with industry, government, or multidisciplinary research teams