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computing frameworks (e.g., MPI, NCCL) and model parallelism techniques. Proficiency in C++/CUDA programming for GPU acceleration. Experience in optimizing deep learning models for inference (e.g., using
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, advancing bioinformatics-driven research, facilitated by exceptional computational infrastructure including a centrally administered high-performance CPU and GPU cluster and network storage. This includes
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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 for scalable and green AI. Use cases in
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in physics, mathematics or any related field; correspondingly, Postdocs hold a PhD or equivalent degree in the abovementioned fields. What we offer State of the art on-site high performance/GPU compute
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, featuring 328 general nodes with 476 TB of RAM, and 448 GPU nodes with 31 TB of memory. We also have an AI/ML cluster, and an AI cluster, with over ~110 PB of storage for HPC computations. Applicants should
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Engineering, or a related field Strong experience in building and optimizing AI systems using PyTorch, TensorFlow, or JAX Practical knowledge of NVIDIA GPU programming (CUDA) and experience with inference
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transcriptomics. Innovative visualization tools and highly automated analytical pipelines powered by GPU technology. Mentorship from experienced scientists in data analysis and management, with an expertise in
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an early member of a friendly, multi-disciplinary team. The project will involve significant coding using the Julia package Molly.jl on both CPU and GPU, as well as addressing relevant computational and
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& GPUs — and deep integration with clinical data including electronic health records A diverse array of ongoing research, education, patient care, and community centered activities that require increasing
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Empire Innovation Professor (Associate Professor) Artificial Intelligence in Biomolecular and Drug D
. This position offers substantial resources, including access to the SeaWulf computing cluster and cutting-edge GPU clusters housed at IACS and CEWIT. The Empire Innovation Professor will join a robust community