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. Ubuntu) is essential.. Knowledge of GPU computing, CUDA programming, model optimization, and industry experience in engineering software development would be an advantage. Excellent written and oral
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or streaming data. Develop parallelized and GPU-accelerated learning modules, ensuring scalability and performance efficiency. Build and maintain robust data pipelines for high-throughput modeling over
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or streaming data. Develop parallelized and GPU-accelerated learning modules, ensuring scalability and performance efficiency. Build and maintain robust data pipelines for high-throughput modeling over
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to deliver responsive, reliable and future-ready IT solutions. Key Responsibilities: • Manage and maintain the centre’s IT infrastructure, including servers, networks, and GPU systems. • Plan and
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. Programming & Software Development: Proficiency in Python, PyTorch, JAX, or other ML frameworks Computing: Some experience with large-scale datasets, parallel computing, and GPUs/TPUs. Algorithm Development
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programme Access to secure clinical and multi-omics data environments Modern GPU, and high-performance computing resources, plus dedicated research-engineering support Close integration with clinicians and
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. Programming experience in C/C++ is necessary while experience in parallel and GPU computing is most desired.PX4, Pixhawk or equivalent. Ability to work well with team members and good communication skills
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programming proficiency in Python and C/C++. Expertise in ensemble learning (e.g., Random Forests, Gradient Boosting, bagging/stacking frameworks). Hands-on experience with parallel or GPU-based computing (CUDA
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good understanding of the physics of scattering and antenna radiation Programming experience in C/C++ is necessary while experience in parallel and GPU computing is most desired More Information Location
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multidisciplinary research programme focused on developing advanced AI-driven methods for neuroimaging, digital phenotyping, and population brain health. This programme brings together expertise in artificial