15 molecular-dynamics-simulation Postdoctoral positions at Carnegie Mellon University
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understanding of placental development through the integration of computational modeling and clinical imaging data within the Biomedical Flows Simulation and Multiscale Modeling (BioSiMM) Lab. Core
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of molecular sensing circuits Designs and carries out experiments in BSL2 lab space Performs quantitative analyses and generates data-rich visualizations from data Demonstrates ability to perform
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of molecular sensing circuits Designs and carries out experiments in BSL2 lab space Performs quantitative analyses and generates data-rich visualizations from data Demonstrates ability to perform
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molecular biology or programming-based image analysis will be much welcome. The project(s) related to this position will be quantifying and redesigning the biophysical and physiological properties
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, background in imaging, computational modeling, and molecular biology. Applications, including a cover letter and a curriculum vitae indicating your interest and relevant training should be submitted
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critically on viable commercial mechanisms that support healthy development for model providers and downstream users. Core Responsibilities: Analyze LLM-based commercial models by collecting, simulating, and
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and validates the performance of molecular sensing circuits Designs and carries out experiments in BSL2 lab space Performs quantitative analyses and generates data-rich visualizations from data
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, Materials Science, or Polymer Science or a relevant field with relevant research experience in polymer synthesis and molecular characterization. Hands on experience with lab automation. Advanced skill level
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Responsibilities Include: Develop computational methods for inference and control that improve the reliable and efficient operation of autonomous agents in complex, uncertain environments. Modeling dynamical systems
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. Modeling dynamical systems Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods