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PhD scholarship in Runtime Multimodal Multiplayer Virtual Learning Environment (VLE) - DTU Construct
for realistic haptic control and feedback, and real-time agent-based simulation for guiding optimal work performance. Following smart serious gaming approaches, novel artificial intelligence forecasts human
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industrial processes. Your research will drive a paradigm shift in how TES systems are modelled, integrated, and controlled within industrial settings. You will develop novel, adaptive, physics-informed models
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to facilitate a rapid and efficient exchange among experimental and computational groups and Devise an approach in invertible predictive modelling that links semiconductor properties to the composition of lead
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science, computer science, applied mathematics or a related field a strong background in machine learning, material modeling, and metals processing, modeling and simulation (This will be a clear advantage
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. The position will involve development of codes/models simulating the nucleation and propagation of stress corrosion cracking in samples under low cycle fatigue conditions, as well as models for linking
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, energy-related datasets. Proficiency in Python, MATLAB, and/or Julia for modeling, simulation, and data analysis. Familiarity with GIS tools (e.g. QGIS), time-series databases (e.g. InfluxDB), and version
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, or related fields Experienced, or willing to become an expert in the sustainable transformation of infrastructure and simulation models for socio-technical systems Proactive, showing initiative and able
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-world medical applications? Do you want to design next-generation protein therapeutics using cutting-edge generative models and validate them in the lab? Join a collaborative PhD project at DTU
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. 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 regular and irregular wave
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to the development of deep learning methods to predict reaction outcomes and optimal reaction conditions for organic reactions. The work will involve model development using Python and/or other programming languages