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contribute to developing and improving the performance of next-generation thermal systems—including HVAC, water heating, and data center cooling—at both the system and component level. This position resides in
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delivering programmatic milestones. Major Duties/Responsibilities: Design, analyze, fabricate and test Fabry-Perot cavity-based fiber-optic sensors to measure the deformation of metal diaphragms for various
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such as quantum and analog computational models. You will explore how compilers, runtimes, and AI-driven agents can co-optimize complex architectures, reasoning across conventional processors (CPUs/GPUs
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methods. Additionally, you will develop/refine finite element and numerical analysis models to portray shear strength response as a function of temperature, and to attempt to ultimately reconcile those
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scalability of simulation workflows via: Parallelization and performance engineering GPU/accelerator optimization Algorithmic innovation Experience applying machine learning or AI to molecular simulation
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component (ELM) and its peatland-specific configuration (ELM-Peatlands), with a focus on improving the representation of nutrient cycling dynamics and their coupling to hydrological and ecosystem processes
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component (ELM) and its peatland-specific configuration (ELM-Peatlands), with a focus on improving the representation of nutrient cycling dynamics and their coupling to hydrological and ecosystem processes
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commercial (e.g. Abaqus, ANSYS, etc.) and/or open-source finite element (FE) codes (e.g., MOOSE, DAMASK, etc.) is required. Experience with microstructural modeling (e.g. crystal plasticity) applied to fatigue
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for radioactive s-, p-, and f-block metal ions. Independently execute and troubleshoot complex, multi-step organic syntheses on the multi-gram scale, including reaction optimization, scale-up, purification, and
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, etc.) composites. Hands-on experience with lab scale polymer synthesis and analysis. Preferred Qualifications: Experience with computer modelling systems such as finite element analysis (FEA