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@stanford.edu (link sends e-mail) and cc eskanlou@stanford.edu (link sends e-mail) Does this position pay above the required minimum?: Yes. The expected base pay range for this position is listed in Pay Range
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platforms. Experience in high-performance computing or working with large-scale simulation environments. Prior work involving model calibration, optimization under uncertainty, or scenario analysis
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propagation problems, stochastic partial differential equations, geometric numerical integration, optimization, biomathematics, biostatistics, spatial modeling, Bayesian inference, high-dimensional data, large
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areas Biomedical applications, social determinants of health or other demographic health areas Spatial microsimulation, spatially weighted regression, combinatorial optimization or Bayesian network
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part of the Dynamical Systems Section. We perform research within a broad range of areas within dynamical systems including modeling, optimization, forecasting, and controlling in both deterministic and
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publication record is encouraged and previous experience in areas such as Distributed control and optimization, Swarms, Autonomous Robots, Embedded systems development, Collaborative and adversarial control
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://martilab.com/ . Trains under the supervision of Prof. Edward Marti including (but not limited to) the following activities: Build and optimize ultrasound rigs and imaging of neonatal and adult rodents (mice and
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under the supervision of Prof. Edward Marti including (but not limited to) the following activities: Build and optimize ultrasound rigs and imaging of neonatal and adult rodents (mice and rats). Develop
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students, interact with collaborators, and adopt and optimize relevant techniques for the project where applicable. Professional development: 5% Requirements PhD in molecular biology, cancer biology
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translate fundamental insights in membrane technology into practical, impactful solutions. Responsibilities and qualifications Develop and optimize highly structured membranes for membrane distillation. Lead