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inhibitors with improved efficacy The project offers a highly interdisciplinary research environment spanning computational chemistry, cell biology, physics, and materials science. The work will leverage GPU
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biology results The project offers a highly interdisciplinary research environment spanning computational chemistry, neuroscience, molecular biology, and psychology. The work will leverage GPU computing
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to the development of advanced language models and derived use cases by focusing on one or more of the following topics in their PhD project: Training and inference of ML models on GPU clusters. Method development
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and optimization strategies for large-scale or streaming data. Develop parallelized and GPU-accelerated learning modules, ensuring scalability and performance efficiency. Build and maintain robust data
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, grant writing, and collaborating with students from different majors on the humanistic or social scientific aspects of sustainability. More information may be found at https://www.uta.edu/academics
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-design with accelerators (FPGAs, GPUs, near-memory systems) to achieve real-time, energy-efficient AI for high-tech industry applications. Work with leading companies like ASMPT and shape the future of AI
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manages model onboarding, platform health, monitoring, and configuration across on-premises GPU systems and cloud providers, ensuring reliability and security. Additionally, the AI Solutions Engineer II
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SecureData4Health (SD4H) OpenStack cloud infrastructure. It currently includes 15,000 VCPU, 60 Petabyte of storage, 30 GPU and is growing as additional academic research projects join. The Software Infrastructure
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will focus on the development of GPU-accelerated GPAW software based on density functional theory (DFT) for constant-potential calculations within a plane-wave framework. The developed software will be
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and intense laser interactions with matter using particle-in-cell (PIC), hydrodynamic, and/or Fokker-Planck open and proprietary simulation packages. Investigate and develop different physical