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simulation models used in rare-event searches, develop machine learning methods for background rejection, and place constraints on new physics using LZ datasets. In addition, the postdoc will lead the
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a member of an inclusive, interdisciplinary research team. The ideal candidate will have experience with flow cytometry, scRNA-seq, and/or animal models. The hiring unit will not sponsor employment
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://med.stanford.edu/wendy-liu-lab(link is external) https://profiles.stanford.edu/wendy-liu(link is external) How to Submit Application Materials: Please send a cover letter and a copy of your CV (please include list
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
analyzing molecular dynamics (MD) simulations to understand water and solute transport through highly crosslinked polyamide systems. The postdoctoral researcher will study polymer chemistry, crosslinking
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approximately 50% clean room work and process development, and 50% optical simulations (Lumerical, mode solvers, FDTD, inverse design, …) and optical experiments. The focus will be on fabricating novel devices
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University and imec. The estimated breakdown of the research activity is approximately 50% clean room work and process development, and 50% optical simulations (Lumerical, mode solvers, FDTD, inverse design
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products) and the host (cells, animals and humans) in synergy with the expertise available within the department; develop holistic models to simulate and steer these interactions; combine our expertise
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the development of AI models for robotic control and the demonstration of these methods via simulation and experiment. Beyond the listed projects, the candidate may contribute to other large-team scientific
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. Knowledge of GWAS analyses (PLINK and SKAT-O), and multivariate and mixed model analyses are also recommended. We are seeking motivated postdocs with strong analytical skills to join our effort and generate
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, to define novel biomarkers, and to identify novel therapeutical targets. We have pioneered in the integration of genetics with omic data to identify proteomic signatures and develop novel predictive models