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, electrical engineering, computer engineering, computer science, aerospace engineering, or a closely related field obtained in the last five years. Demonstrated experience developing robotic systems using ROS 2
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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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. Experience with developing machine-learning surrogates for structure-property relationship, generative AI models, material representations, machine learning force-fields (especially extensions to spinful
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. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process design is preferred. Excellent written and oral
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, LeafWeb, Sapfluxnet, PSInet) to translate trait variation into model parameter priors and functional constraints, and to explore parameter relationships with environmental conditions Hybrid modeling
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processing (e.g., arc melting, heat treatment) and relate processing to performance. Apply analytical and modeling approaches to interpret experimental results. Collaborate with internal and external research
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-body ab-initio methods for description of electronic, magnetic, and vibrational properties in a range of materials Expertise with artificial intelligence and machine learning approaches will be also
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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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Demonstrated experience with low-background detectors, cryogenic liquid scintillators, plastic scintillators, additive manufacturing, and/or Ge detector technology Advanced computer programming experience
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modeling and networked biological systems. You will work at the intersection of high-performance computing (HPC), computational biophysics, and machine learning, leveraging leadership-class computing