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Field
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, advanced optimization and control of semiconductor manufacturing processes and systems. Experience in working with advanced AI/ML software packages and super-computing cluster systems, including Hadoop
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for an ultra-high vacuum synchrotron end-station using CAD software. - Run, optimize, and document data acquisition codes (LabVIEW and Python) - Aid NIST staff in developing plans
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) and a track record of productive collaboration within interdisciplinary teams. Core Skills: Experimental Design & Troubleshooting: Ability to conceptualize, execute, and optimize complex in vivo (mouse
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distribution systems, EV charging modeling, distributed energy resources, optimization, control, machine learning, hardware-in-the-loop simulation. Expertise in programming languages such as Python, C
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Responsibilities Develop and optimize protein purification protocols for key stress response factors Establish in vitro reconstitution systems to study protein-metabolite interactions Design and implement genetic
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, develop, and optimize new methods and techniques to address critical project or functional area needs. Participants will improve existing or develop new laboratory methods and processes, read and adapt
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that underpins the scientific research of the collaboration. Project Plan: The work will build on PML's current optomechanical accelerometry research and develop sensing optimized for inertial measurement. New
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research interests in one or more of the following subfields: scientific machine learning, optimization, deep learning, uncertainty quantification, (Bayesian) inverse problems, reduced order modeling, high
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, develop, and optimize new methods and techniques to address critical project or functional area needs. Participants will improve existing or develop new laboratory methods and processes, read and adapt
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datasets as well as optimally leveraging integration with existing genomics datasets. The role will often involve rapid prototyping in support of a dynamic, fast-moving experimental program; it is focused