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Language Models (LLMs). Distributed Machine Learning: Specialization in data parallelism, model-parallelism, and collective communication strategies in large-scale environments. Proficiency in frameworks
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devices. Proficiency in surface and interface characterization using atomic force microscopy. Experience in materials characterization techniques such as Raman and scanning electron microscopy. Data
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
that can incorporate multi-scale computational simulations to aid with data fusion across multiple modalities of experiments with the final goal of discovering novel materials phenomena or even new materials
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compilers and runtimes can unify classical, quantum, and analog execution models under a shared optimization framework. Basic Qualifications: Ph.D. in Computer Science, Computer Engineering, or a closely
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qubit engineering to quantum algorithmic analysis to applications across the physical sciences (condensed-matter or high energy physics, data science). You will Interpret, report, and present research
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Experience with deep learning frameworks such as PyTorch or TensorFlow Exposure to AI-enabled scientific workflows that couple simulation with data-driven modeling, including emerging approaches involving