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qualified Postdoctoral Research Associate. The incumbent will join a collaborative team to combine synthetic magnetosome biogenesis and computationally optimized cobalt-binding peptides to enable efficient
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analyses to support strategic and tactical decisions. Adheres to University and unit-level policies and procedures and safeguards University assets. This list of duties and responsibilities is not intended
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validation and Pileup modeling), the MET High-Level Trigger validation, optimization and performance studies, and to the heterogeneous computing where the focus will be to work on to the current efforts
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. The group will be contributing to the Physics Modeling (MC software, MC validation and Pileup modeling), the MET High-Level Trigger validation, optimization and performance studies, and to the heterogeneous
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optimization under uncertainty, constrained query evaluation, or the design of efficient, explainable, and scalable query engines. The successful applicant will help design and build novel systems and algorithms
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opportunity for someone who thrives in an interesting and challenging work environment. Core responsibilities include: Research in the intersection of discrete optimization and machine learning, including
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, the focus will be on implementing inverse design, optimization, and/or machine learning for designing and optimizing devices in integrated photonics, which will be subsequently fabricated and tested by our
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inverse design, optimization, and/or machine learning for designing and optimizing devices in integrated photonics, which will be subsequently fabricated and tested by our experimental partners in Metapix
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Deploy, commission, and operate TES X-ray microcalorimeter spectrometers at APS beamlines, including cryostat integration, beamline interfaces, alignment, and stability optimization. Perform pathfinding
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include the following activities: Material development, synthesis and method optimization Metal nanoparticle catalyst and ceramic material and cell component comprehensive characterization and benchmarking