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Job Description DTU, Department of Civil and Mechanical Engineering, the Section for Manufacturing Engineering invites applications for a PhD position (3 years) on the topic of simulation
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models and reinforcement learning models for 3D graphs of materials to explore vast inorganic chemical spaces and design synthesizable energy materials. You will couple such models with physics simulation
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surgical robots across various surgical applications, using techniques such as advanced sensing, AI-based and reinforcement learning (RL)-based control, and soft continuum robot simulation. The starting date
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and reinforcement learning (RL)-based control, and soft continuum robot simulation. The starting date is expected to be February 15, 2025, or as soon as possible thereafter and will be agreed with
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PhD scholarship in Corrosion Mechanisms of Power Semiconductor Device and Components - DTU Construct
, gases and applied potential conditions. The project will also include the development of advanced simulation models to characterize and predict moisture transport through gel substrate and interfacial
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. Motivated letter of application (max. one page) 2. CV incl. education, experience, language skills and other skills relevant for the position 3. Certified copy of original Master of Science diploma and
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of the jobpost. Further information We recommend that you save a copy of the job posting, as it will be removed once the application deadline has passed. The assessment of candidates for the position will be
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. We are a team of currently about 20 researchers, engineers and PhD students, specialized in robot solutions involving modelling, simulation, and control of advanced robot technologies (systems and
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of autonomous behavior and their representative demonstrations in challenging simulation environments. IMADA has the unique feature of bringing mathematicians and computer scientists together within a single
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for light, water, and nutrients. Integrate advanced sensors (e.g., spectral, LiDAR) and simulation tools to guide robotic actions for yield optimization and enhanced biodiversity. Apart from the topics above