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Field
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part of the core PLI team, which includes top-tier faculty, research fellows, scientists, software engineers, postdocs, and graduate students. Fellows will have access to the AI Lab GPU cluster (300
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) and reproducible research practices Desirable criteria Experience working with generative models or large language models Experience with large scale GPU-based model training and cloud computing
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for improved interpretability and generalization. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability to work collaboratively in interdisciplinary and cross
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numerical solvers for 2D and 3D phase field models Develop HPC-ready simulation pipelines for large-scale rupture and fracture-fluid systems Optimize performance for modern architectures including GPUs and
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and Machine Learning tools and algorithms to solve hydrology and water resources problems. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability
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algorithms to solve hydrology and water resources problems. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability to work collaboratively with
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molecules [doi.org/10.1021/jacs.2c07572 , doi.org/10.26434/chemrxiv-2023-5kl9x ]. (iii) Developing GPU-accelerated multireference methods to improve the accuracy and robustness of current state-of-the-art
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development skills Model deployment (e.g., ONNX, TensorRT) Edge computing or embedded vision systems (e.g., NVIDIA Jetson Nano) Real-time processing and GPU acceleration Experience working on industry R&D
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(LLMs); Configure and optimize cloud computing solutions or on-premise infrastructures that ensure high availability and scalability; Implement tools for efficient resource management, such as GPU
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vision systems (e.g., NVIDIA Jetson Nano) Real-time processing and GPU acceleration Experience working on industry R&D projects Key Competencies Able to build and maintain strong working relationships with