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spatiotemporal distributions of thermal variables, suffer from limited predictive accuracy and prohibitively long computational times, making them unsuitable for real-time process control. Artificial intelligence
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requirements while enabling fully electrified actuation and control. Although research has demonstrated stable levitation and contact-free pumping in laboratory-scale systems, reliable design methods for
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model predictive control, machine learning, or reinforcement learning. Experience with IoT, edge computing, and cloud integration. Proven ability to contribute to research funding proposals
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Biomaterials! PhD Position – AI-Driven Multimodal Analysis and Predictive Modelling of Biomaterials Apply for this job See advertisement About the position Biomaterial Science (BIOMAT) / Institute of
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-based predictive software able to capture the desired photophysical properties. On the other side, search in chemical space will take advantage of high throughput screening, machine learning and
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microbiomes, and predictive models linking molecular and clinical data to cancer risk stratification. The work will be carried out in close collaboration with computational researchers at DTU and
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campus. This project leverages the Applied Federated Energy Research Accelerator (AFERA) framework—a novel paradigm integrating Agentic AI, applied energy solvers, digital twins, and control automation
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and fully editable control over modelling, animation, visual effects and compositing. Building these systems requires considerable expertise and can be repetitive, as each task may involve constructing
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 2 months ago
campus. This project leverages the Applied Federated Energy Research Accelerator (AFERA) framework—a novel paradigm integrating Agentic AI, applied energy solvers, digital twins, and control automation
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, you will study solid CO₂ layer growth and the thermal and concentration boundary layers near the surface. Your work will contribute to a predictive understanding of CO₂ desublimation and support the