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applied research on AI-driven and AI-enhanced industrial energy systems optimization modeling, material flow analysis, and supply chain analysis of industrial commodities and critical materials
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systems. In parallel, they will design and develop agentic AI and physics-aware AI models to accelerate discovery and deepen mechanistic insight in catalysis. This work will be carried out in close
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(DUSTIEAIM) observational datasets, together with advanced modeling frameworks such as Energy Exascale Earth System Model (E3SM) or data-driven AI models. Position Requirements Completed or soon-to-be
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laboratory workflows. The position will focus on building the data resources, predictive models, and closed-loop decision frameworks needed to accelerate experimentation and advance next-generation autonomous
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centralized. The successful candidates will work at the intersection of federated learning, foundation models, multimodal biomedical AI, privacy-preserving machine learning, continuous learning, and agentic AI
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and will be expected to contribute to a safe, collaborative, and results-oriented research environment. The work will emphasize process engineering, experimental systems, and data-driven evaluation
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. Proficiency in simulation tools used in analyzing vehicle energy consumption. Passion for and experience in data-driven modeling and analysis. Demonstrated ability to perform vehicle modelling and simulation as
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and structural behavior of these strategically important elements in geological materials. We are looking for a creative and driven experimental scientist with experience in synchrotron scattering and
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of these systems are insufficient to cover the diversity of human-driven experimental activities. The development of multi-appendage, dexterous robots with embodied intelligence is a key to closing this gap
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offer a unique opportunity to pioneer multimodal, physics-driven synchrotron research that bridges defect dynamics and functionality in emerging microelectronic materials. Key Responsibilities: Design and