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is a fully verified opensource inference stack with competitive performance). Further information on the research of Dr. Marios Kogias can be found at https://marioskogias.github.io/ . What we
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is available at https://colalab.ai/ . At EI, the group will develop an ambitious Generative Digital Biology (GDB) programme, developing multimodal, cross-organism and experimentally grounded AI systems
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that learn from biological data, support hypothesis generation, guide experimental design and accelerate discovery. Further information is available at https://colalab.ai/. The group will have access
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Position Details Position Information Recruitment/Posting Title Lead Software Developer – GPU-accelerated Free Energy Simulation and Machine Learning Methods Department Quantitative Biomedicine Inst
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simulation, Cosserat rod theory to support hands-on class activities and development of NVIDIA GPU-based course modules. • Availability to schedule, coordinate, and oversee student visits to operating rooms
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, please visit: http://uhr.rutgers.edu/benefits/benefits-overview . Posting Summary The Rutgers Artificial Intelligence and Data Science (RAD) Collaboratory is seeking to appoint two highly motivated
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for advanced research. A7. Knowledge of scaling and optimising software to take advantage of GPU / HPC infrastructure. Desirable: B1. Knowledge of Trusted Research Environments out with or within an HPC
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the group, translating cutting-edge deep learning algorithms into robust, maintainable analysis tools and pipelines Implement and optimize large-scale deep learning workflows on GPU-enabled High-Performance
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with AMD MI300A GPU+CPU. 2. Perform benchmarking studies to enhance scalability and achieve high node-level efficiency, surpassing existing AMR frameworks. 3. Contribute to communication optimizations
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or machine learning infrastructure, model deployment environments, or GPU-enabled systems. Familiarity with CI CD pipeline design and automation. Experience with hybrid infrastructure spanning on-premises and