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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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chemistry and experience with quantum chemistry packages (e.g., Molpro, NWChem) Strong skills in developing and implementing computational and numerical methods; familiarity with parallel computing on CPU/GPU
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). Expertise in data and model parallelisms for distributed training on large GPU-based machines is essential. Candidates with experience using diffusion-based or other generative AI methods as
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five years. Demonstrated expertise in computational mechanics and numerical modeling Experience in polymer composite manufacturing processes Experience with simulation tools for thermomechanical analysis
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of numerical quantum many-body methods to study model Hamiltonians. Strong background in linear algebra. Preferred Qualifications: Experience with density matrix renormalization group and tensor network
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advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
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for massively parallel computers. Experience with quantum many-body methods. Preferred Qualifications: A strong computational science background. Familiarity with coupled-cluster method. Understanding