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Field
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simulations and multiscale spatial-omics data. • Integrate uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments
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on classical supercomputers. Design and optimize classical simulation algorithms for quantum circuits. Explore approximate classical simulation algorithms (e.g., tensor networks, circuit cutting) to improve
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complex instruments and run simulations to accelerate discovery. This involves navigating vast parameter spaces, identifying rare or transient phenomena, and dramatically optimizing the use of precious
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(e.g., superconducting quantum processors). Validate, analyze, and interpret experimental data and results. Simulate the performance of quantum circuits on classical supercomputers. Design and optimize
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degree and experience in a relevant field. Ideal applicants will have experience in energy-system transition modeling, linear and mixed integer optimization programming, energy-related techno-economic
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characterize polymer and dendrimer-based nanomaterials for targeted drug, and gene delivery. Develop and optimize linker chemistries and conjugation strategies for attaching therapeutics, targeting ligands, and
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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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continuation. Job duties: Design, synthesize, and characterize polymer and dendrimer-based nanomaterials for targeted drug, and gene delivery. Develop and optimize linker chemistries and conjugation strategies
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and has a zest for organization and accomplishing goals. Job tasks include optimizing intrinsic labeling systems (13CO2, 2H, 57Fe, etc.); developing high-throughput extraction, sample clean-up, and
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Department of Chemical and Biological Engineering at Princeton University. The position is in the broad area of renewable energy systems synthesis, analysis, and optimization. The goal of the project is to