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, you will have early access to the Empire AI clusters, utilizing state-of-the-art GPU architectures to push the boundaries of structural biology. This position is a prestigious Empire AI Fellowship
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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). Experience training AI models on GPUs. High motivation for research and a commitment to publishing at top conferences. Proven experience in submitting research to top-tier venues, such as ECCV, CVPR
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• Execute large-scale simulations on CPU and GPU-based HPC clusters • Analyze results, generate technical reports, and deliver project outcomes on schedule • Prepare scientific reports and publish in
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adaptive optimization during needle insertion, integrating live ultrasound imaging with GPU-accelerated dose calculation and optimization. The Postdoctoral Research Associate will join a multidisciplinary
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scalability of simulation workflows via: Parallelization and performance engineering GPU/accelerator optimization Algorithmic innovation Experience applying machine learning or AI to molecular simulation
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hardware architects to establish how agentic AI and these languages co‑design with heterogeneous HPC systems (CPUs, GPUs, PIM, AI accelerators). Study performance and portability tradeoffs, leveraging
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of cores, and a growing GPU cluster containing thousands of high-end GPUs. Depending on the day, we might be diving deep into market data, tuning hyperparameters, debugging distributed training performance
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. Qualifications: Familiarity with machine learning interatomic potentials, CPU and GPU parallelization, knowledge of LAMMPS and molecular dynamics, experience with first principles calculations of dielectric and
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on small test clusters. Test computational performance and resolve technical challenges on significantly larger models of selected quantum materials. Work on speeding up Krylov solvers on GPUs. Demonstrate