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developing advanced AI/ML models for applications in physics, chemistry, or materials science Experience with periodic simulation codes such as VASP Proficiency in Python programming Excellent written and oral
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using computing clusters for simulation and data analysis. Experience with accelerator modeling and simulation codes, such as Elegant and Cheetah. Experience with SDDS, Tcl/Tk, and scripting in Linux
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related field. Experience with finite element simulations and developing constitutive models. Knowledge of high temperature creep crack growth. Knowledge of engineering design codes such as the ASME Boiler
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with DFT or electronic-structure codes such as VASP, Quantum ESPRESSO, CP2K, ABINIT, GPAW, Gaussian, ORCA, Q-Chem, or related packages. Strong materials science or chemistry domain knowledge, such as
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. Experience in numerical methods and CFD development using mesh-based scientific codes. Expertise in the lattice Boltzmann method (LBM) as evidenced by their publications High performance computing (HPC
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have a strong background in fundamental electrochemistry, with preferable hands-on expertise in computational materials science. The applicant should be well versed in code development, application of AI
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addition to research activities, the candidate will be expected to play a pivotal role in mentoring students and fellow post-docs on various material-centric projects, fostering an environment of knowledge sharing and
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experimental cycle from real-time X-ray measurements to post-experiment reconstruction: Develop learning-enabled algorithms for 3D reconstruction of noisy and heterogeneous synchrotron datasets. Implement
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techniques and strategies for electrochemical measurements and post-mortem analysis. Write reports and high impact papers, and present research at conferences. Develop or support the development
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ideas within the scope of position posting (3 page max).** Application review will commence on June 08 latest and resume until position is filled. Inquiries regarding scientific scope of the position can