39 scholarship-phd-agent-based-modelling Postdoctoral positions at New York University
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is looking for a highly motivated and independent individual to work as a Postdoctoral Associate. This position is for a post-PhD trainee preparing for a research scientist career path. The planned
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engineering. Required Qualifications: A successful applicant must have a PhD in Geotechnical Engineering, or a related field with a focus on one or more of the following: Centrifuge experimental testing (design
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when extending an offer. The ideal candidates will hold a PhD, have multiple years of prior research experience using a model organism, and a track record of peer-reviewed publications. Prior experience
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Lab The EMERGE lab at NYU is seeking to hire a postdoc to work on scaling and deploying end-to-end RL planning agents for autonomous vehicles. Based on prior work on creating high performing self-play
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brucite (Mg(OH)₂) and CaCO₃ to produce innovative building materials, including RMC- and BCSA-based composites, (iii) studying carbonation and hydration mechanisms in these sustainable binders
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-cell or extracellular recording) Genome editing and disease modeling (e.g., CRISPR/Cas9, TALEN, base-editing) Applicants are invited to submit their applications through Interfolio by clicking the "Apply
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/Python), deployment (to the agents' onboard computational hardware using the Robot Operating System), and experimental testing (on unmanned vehicle swarms). A solid competency with the standard
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Post-Doctoral Associate in the Center for Translational Medical Devices (CENTMED) - Dr. Panče Naumov
, compression molding, injection molding, and fused deposition modeling. This position requires the preparation of ISO/ASTM test specimens for mechanical (tensile and flexural) testing of polymers and composites
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biomedical and mechanical engineers, biologists, and pharmacists. Expectations Candidates will be responsible for working with a unique transgenic mouse model that enables specific cardiomyocyte reprogramming
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mesoscale fractal geometry, creating physics-informed neural network models to analyze turbulent structures, and comparing simulation results to astronomical observations to develop methods for inferring