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Field
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the emphasis of the position will be on the development of nanomaterials for AM and understanding of AM process optimization, functional materials design and compositional grading, electrochemical and
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, biologists, and data scientists. The emphasis will be on enabling high-fidelity image reconstructions from sparse and noisy data, leveraging state-of-the-art methods in compressed sensing, optimization, and
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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. Experience working with rodent models. Experience with mammalian cell culture. Experience with live-cell or in vivo microscopy. The optimal candidate will have a background in neuroscience or cell biology, as
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nanomaterial-nucleic acid hybrids for optimal device bio- and gas sensing, as well as for optoelectronic applications. About You The candidate will have experience in materials science research and ideally DNA
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smectite, sepiolite, and layered double hydroxide. -Summarize the MD data to find the optimal mineralogy properties need to maximize the binding efficiencies of the minerals for the toxins. -Summarize
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nanomaterial-nucleic acid hybrids for optimal device bio- and gas sensing, as well as for optoelectronic applications. About You The candidate will have experience in materials science research and ideally DNA
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on Compact Manifolds with Novel Feedback Coupling and Complex Network Optimization." Specifically, the selected candidate will address Research Objective (RO) (2) Investigate the dynamical phenomena of orbital
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-driven catalytic systems and/or electrochemical analysis (e.g., GC-FID, cyclic voltammetry, electrochemical workstation) Strong experimental design skills, including optimization of material synthesis
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), and cell free methods. Key Responsibilities: Development and optimization of vector constructs and expression condition characterization of protein yields and quality, and large-scale protein production