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Field
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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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for Science @ Scale: Pretraining, instruction tuning, continued pretraining, Mixture-of-Experts; distributed training/inference (FSDP, DeepSpeed, Megatron-LM, tensor/sequence parallelism); scalable evaluation
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consistently ranks among the top 10 universities in the annual U.S. News & World Report survey. Penn has 12 highly-regarded schools that provide opportunities for undergraduate, graduate and continuing education
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consistently ranks among the top 10 universities in the annual U.S. News & World Report survey. Penn has 12 highly-regarded schools that provide opportunities for undergraduate, graduate and continuing education
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APPLICATION INSTRUCTIONS: CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process . Please do not apply
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one or more of the following areas: Programming, Algorithms and computational complexity, Programming languages and compilers, Computer graphics, and Parallel and GPU computing. A typical teaching
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Experience with designing and implementing HPC systems and solutions. Familiarity with parallel computing, distributed systems, and cloud integration. COMMITMENT to DIVERSITY and CAMPUS VALUES A successful
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Description Electromagnetic techniques can provide a means for rapidly analyzing or processing biochemical samples in a manner that can be readily scaled up to handle large numbers of samples in massively
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, enhanced sampling, QM/MM) Experience improving performance and scalability of simulation workflows via: Parallelization and performance engineering GPU/accelerator optimization Algorithmic innovation
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for Science @ Scale: Pretraining, instruction tuning, continued pretraining, Mixture-of-Experts; distributed training/inference (FSDP, DeepSpeed, Megatron-LM, tensor/sequence parallelism); scalable evaluation