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-funded fellow, 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
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) environments, GPU-enabled systems, and cloud-based resources for large-scale data analysis and model development. Using these rich data resources, we develop and apply advanced AI/ML methods to model tumor
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-performance computing environments and GPU computing. Proven experience in weather and climate models development and applications. Experience in machine learning, deep learning, or AI applications
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), numerical methods, and scientific software development. Proficiency in C/C++ and Python, with experience in HPC environments (e.g., MPI /OpenMP; GPU experience a plus). Record of peer-reviewed
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systems • Strong computational background Preferred Qualifications • Experience with GPU programming, shaders, or advanced rendering techniques • Experience integrating external APIs or live data
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implementing high performance signal processing algorithms on resource constrained hardware Experience using FPGA development tools such as Vivado and LiteX Experience using GPU compute frameworks such as
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pretraining and fine-tuning is a plus · Experience with multi- GPU or distributed training is a plus · Ability to work independently as well as part of an interdisciplinary team in a fast-paced
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-image registration , high-performance computing, or GPU-based computation. Familiarity with machine-learning or deep-learning methods for medical-image segmentation. Previous experience in thermal
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. Experience or training and deploying models on Graphics Processing Unit ( GPU ) hardware. Working Conditions Needs to be able to successfully perform all required duties. Office/research environment
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and graduate research assistants, and presenting at design reviews and project milestones. Conducts benchmarking and performance evaluation of LLM serving and agentic workflows on high-performance GPU