141 molecular-modeling-or-molecular-dynamic-simulation PhD positions in United States
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Department:Biochemistry and Molecular Biology Salary Salary:Commensurate Position Details Full/Part Time Status:Full Time Percent Time:100% Position Description: TITLE: POSTDOCTORAL RESEARCHER (Wallrath and Drack
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Full Time or Part Time? Full Time Shift Day Work Schedule Summary VP Area U of U Health - Academics Department 01337 - Molecular Medicine Program Location Campus City Salt Lake City, UT Type
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to therapies and vaccines against human diseases. We are a team of highly interactive investigators that have expertise in immunology, molecular biology, virology, microbiology, structural biology, computational
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. Desirable Qualifications: Desirable Qualifications: Laboratory skills:in vitro and in vivo studies, flow cytometry, andtechniques in molecular biology Experience with mouse models. Competency in SolidWorks
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, which has seen little improvement in patient survival over the past decades. Utilizing genetically engineered mouse and human glioma models, our lab investigates how glioma cells interact with the tumor
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available to conduct research on chronic pain. Using animal pain models, behavioral tests, calcium imaging, patch-clamp, optogenetics, neuroanatomy, molecular biology, the successful candidate is expected
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locomotion. Apply machine learning and machine vision algorithms to track body and limb movements. Use biomechanical modeling to analyze walking data and fit locomotion models. Operate a force sensor to
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machine learning techniques to predict market behavior and generate alphas Execution. Create strategies to execute on modelling ideas under simulated competition Evaluation. Backtest ideas using historical
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machine learning techniques to predict market behavior and generate alphas Execution. Create strategies to execute on modelling ideas under simulated competition Evaluation. Backtest ideas using historical
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systems for both learning and dynamical systems. This work will include new approaches to online learning of optimal policies for hierarchical cyber-physical systems, mixing components of learning and