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cutting-edge experimental and computational technologies. Our aim is to dissect dynamics and cellular programmes active during human blood lineage development and to decipher how haematopoietic
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to apply. We seek candidates with expertise in some or all the following areas: density functional theory, deep learning, high-throughput simulations, molecular dynamics, and materials chemistry. Strong
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researchers will work in a dynamic team of staff scientists at Argonne National Laboratory. Within the team we have extensive experience with large scale molecular dynamics simulations, first principles
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, molecular dynamics simulations using ab initio and machine-learning potentials, and the development or application of machine-learning tools for feature extraction, property prediction, and inverse molecular
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/TranSIESTA), or Molecular Dynamics (including hybrid QM/MM or ML-IP simulations) - Apply for computational resources in HPC facilities when needed. - Prepare periodic reports of the results and provide
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of molecular reactions occurring at the surface of various materials. In addition, computational fluid dynamics (CFD) simulations combined with microkinetic modeling will be carried out to study the heat
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molecular dynamics simulations. This position emphasizes research in the modeling of complex chemical systems, where the candidate will integrate advanced simulation techniques with modern machine learning
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Hydrogen is expected to play a key role in the energy and fuel mix of future sustainable transport systems. However, due to its small and light molecular structure, hydrogen exhibits significantly
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plasmas, including via methods based upon Molecular Dynamics and Particle-in-Cell techniques as well as experiments. We also envision that the ideal candidates will provide support to experimentalists and
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the performance and scalability of large-scale molecular dynamics simulations (e.g. LAMMPS) using machine-learned potentials (e.g. MACE) through algorithmic improvements, code parallelization, performance analysis