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Field
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European partners, supervise MSc and PhD students, and contribute to the development of innovative battery and charging solutions to accelerate the energy transition to climate-neutral cities. The research
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Requirements for the candidate – the candidate: holds PhD in chemical or biochemical sciences or related fields; has solid knowledge and experience at work in peptide synthesis and purification, biochemistry
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or equivalent Skills/Qualifications Requirements for the candidate – the candidate: holds PhD in chemical or biochemical sciences or related fields; has solid knowledge and experience at work in peptide synthesis
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, electricity prices, and hydrogen demand, considering system efficiency, flexibility, production, and operating costs. Validate and benchmark the developed framework using project data, high-fidelity simulations
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and numerical methods for many-body dynamics beyond the reach of standard classical simulation. About the role We have a high preference for a candidates who is willing to start imminently, and before
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scales from microchannels to full rack systems (5.) Develop physics and AI-based models to elucidate physics, perform optimization, and create surrogate models for system level simulators (6.) Participate
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your relevant, demonstrable expertise aligns with the project’s goals; (2) how you would approach the described task(s); the names and contact details of at least two references; a copy of your PhD
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measurements and/or Computational Fluid Dynamics (CFD) simulations. Specifically, you will assess where simplified assumptions in the model fall short. You will then train a machine learning model (such as PySR
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electrocatalysis: The research involves the application and development of ab initio methods, molecular dynamics and kinetic Monte Carlo simulations to model electrocatalytic oxidation and reduction reactions
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National Aeronautics and Space Administration (NASA) | Hampton, Virginia | United States | 18 days ago
microstructure remains uncharacterized. Understanding the sensitivity of model predictions to these unobserved subsurface features is critical for establishing the fidelity and reliability of simulation-informed