97 modal-time-freqeuncy-artificial-intelligence PhD positions at Technical University of Denmark in Denmark
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of Professional Associations. The allowance will be agreed upon with the relevant union. The period of employment is 3 years. The starting date is 1 January 2026 (or according to mutual agreement). The position is
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sections. We broadly cover digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT
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PhD scholarship in Runtime Multimodal Multiplayer Virtual Learning Environment (VLE) - DTU Construct
feedback, and real-time agent-based simulation for guiding optimal work performance. Following smart serious gaming approaches, novel artificial intelligence forecasts human behavior and provides active
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research sections. We broadly cover digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet
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for realistic haptic control and feedback, real-time agent-based simulation for guiding optimal work task performance. Following smart serious gaming approaches, novel artificial intelligence forecasts human
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undermine this future. Can you see how Machine Learning, Computer Vision, and Robotics can open up opportunities for autonomously operating agricultural robots? Are you passionate about making agriculture
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and photon indistinguishability towards unity. The primary responsibilities of the PhD students include: Employ numerical simulation techniques based on Fourier Modal Method and the Finite Element
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in machine learning and artificial intelligence Experience with numerical analysis and scientific computing Knowledge of power systems and renewable energy technologies Experience in power system
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change. Engineering and applied physics – including the design of next-generation artificial phosphors and radiation dosimeters for use in medicine and other industries. Responsibilities and qualifications
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with intelligent, data-driven emergency planning, aiming to develop robust tools for communities and authorities to prevent and mitigate the impact of natural disasters such as wildfires. Working closely