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agents for X-ray spectroscopy by integrating large language models (LLMs) with physics-aware spectroscopy workflows. The researcher will work closely with a multidisciplinary team of X-ray physicists and
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, PyTorch, JAX etc. Experience with modern AI concepts such as large language models (LLMs), vision-language models (VLMs), model context protocol (MCPs), and the development of agentic AI tools. Skill in
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reinforcement learning Experience with high-performance computing, physics-based simulations, and multimodal data workflows Demonstrated ability to train and deploy AI/ML models using simulated and experimental
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novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg
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databases and retrieval-augmented generation (RAG) and integrate agentic AI systems to meet the demands of large-scale fine-tuning and inference. The postdoc will also work on design and implement agentic-AI
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an exciting approach to agentic, fully autonomous thin film development using a combination of automated electroplating, in-operando measurements, and AI driven algorithms. He or she will work with a team of