53 modelling-and-simulation-of-combustion-postdoc PhD positions at Technical University of Denmark in Denmark
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with regular waves. Extending the model to full two-way coupling, allowing feedback from flexible vegetation on wave-induced flow. Applying the fully-coupled model to simulate interactions under both
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advanced materials. Experienced in both strain development and related modelling & data analysis. Experienced with processes of biomanufacturing, including fermentation, downstream processes and scale-up
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. Experience in modeling biological processes. Experience in use of process simulation tools such as Matlab, Aquasim, Superpro Designer or similar. Expertise within fermentation technology and/or gas
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Job Description The project takes place in the Quantum Light Sources group at DTU Electro, where we design, model, fabricate and test sources of single photons or entangled photon pairs
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and operation of building HVAC systems, these technologies support both energy efficiency and flexible demand objectives. Model predictive control (MPC), which involves physics-based building energy
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microbial metabolites and its effect on chronic kidney disease and cardiovascular complications, using an in vivo model of chronic kidney disease. Responsibilities and qualifications As a PhD student, you
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modelling using existing models and using AI based tools. The focus of the work will be to cater to the needs to high voltage/power in power electronic systems, while avoiding humidity and gas exposure
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mathematical foundation of machine learning models. You will be responsible for developing scientific machine learning methodologies enabling new approaches for solving machine learning problems including
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simulation/theory of 2D materials and devices, within electronics, photonics and mass transport. Biophysics and Fluids with a focus on fluid and soft-matter dynamics on small length scales, often with life
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research focus will include some of the following topics: Advanced sensor fusion and multimodal AI models for robotic intercropping. Self-supervised learning will generate multimodal agricultural pre-trained