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, including pretrained universal models and models tailored to specific chemical and materials systems. Develop, debug, profile, and optimize scientific codes and workflows on massively parallel computing
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for Computational Sciences. They will collaborate with leading computer and computational scientists at ORNL and external collaborators in the development and application of new computational techniques specifically
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members Sriram Pemmaraju and Sourya Roy on sampling problems in the distributed and parallel computing setting. The ideal candidate will have research experience in sampling algorithms and related areas
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laser arrays Optimization and acceleration of existing simulation codes Parallel computing and high-performance computing (HPC) GPU acceleration, where appropriate Development of scalable numerical
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integration of novel computer architectures for advanced edge computing scenarios. The research involves design space exploration and the implementation of digital systems using FPGAs, with potential for future
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(e.g. weather, hydrology). 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
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machine operation. These tools will allow operators to adapt to varying beam conditions, mitigate the impact of beam-beam effects, and suppress resonance phenomena. This adaptability is essential
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architectural design decisions for massively parallel heterogeneous systems, including pathways that integrate quantum or analog co-processors. Performance Engineering at Scale: Profile, model, and optimize
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their data pipeline. These foundation models will be used to improve the operation of cryoSTEM microscopes when applied to biological samples. Parallel Model Training: Deploy parallel computing systems
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used to improve the operation of cryoSTEM microscopes when applied to biological samples. Parallel Model Training: Deploy parallel computing systems for training large-scale cryoSTEM foundation models