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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
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Strong foundation in CFD, Programming proficiency such as Python, AI/ML techniques, Experience with parallel computing on CPU/GPU cluster, use of CUDA, MPI is a plus. Experience Experience with open-source
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developments; Significant experience with the development of custom modules using GPU-accelerated APIs for deep learning (e.g., Pytorch); and Publications in top-tier venues in Machine Learning and/or Signal
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, postdocs, and graduate students. Fellows will have access to the AI Lab GPU cluster (300 H100s). Ideal candidates will have a strong interest and proven experience in designing, understanding, or engineering
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: Proficiency in Python, PyTorch, JAX, or other ML frameworks - Computing: Experience with large-scale datasets, parallel computing, and GPUs/TPUs. - Algorithm Development: Ability to develop and optimize Machine
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, postdocs, and graduate students. Fellows will have access to the AI Lab GPU cluster (300 H100s). Ideal candidates will have a strong interest and proven experience in designing, understanding, or engineering
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containing plasma in fusion reactors, revolutionizing material design and structure, novel electronics and bioengineering applications. AI Postdoctoral Research Fellows will have access to the AI Lab GPU
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. • Solid knowledge of numerical methods, including optimization, linear algebra, and geometry processing. • Experienced on GPU programming, familiar with cuda thread scheduling, allocation, and
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to support model-based control of Deep Brain Stimulation (DBS) and personalised treatment strategies for Parkinson's disease. Develop and optimise scalable data processing pipelines, including GPU acceleration
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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