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Field
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Selectris energy filter, Vitrobot Mk. IV, GPU- and data storage-cluster and ready access to Titan Krioses at the Francis Crick Institute, LonCEN consortium, and the UK national cryoEM facility at eBIC. What
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showcases and visualisations; and scaling and optimising data science and AI research to leverage HPC and GPU infrastructure. This post is full time and open ended (permanent) Apply online at: Research
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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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, with a focus on performance, efficiency, and scalability. We own the company's overall strategy for exploring innovative hardware technologies for CPUs, GPUs, memory, storage, and Meta's custom AI chips
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and train CNN and SNN models utilizing frameworks such as Keras, PyTorch, and SNNtorch Implement GPU acceleration through CUDA to enable efficient neural network training Apply hardware-aware design
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neural networks to harden object detection and semantic segmentation, as well as maintaining the real-time performance of the hardened AI computing on embedded GPUs (NVIDIA Jetson AGX Orin). RGB camera and
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for GPU-accelerated applications. Data Engineering Tools: Proficiency in data engineering tools, including Apache Airflow for workflow orchestration, and message brokers like RabbitMQ or Kafka
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includes: A Master’s degree Training and optimizing ML algorithms on GPU hardware architectures, specifically NVIDIA based Working with geo-spatial data Statistics, multivariable calculus, and linear algebra
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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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. Demonstrated history in Astronomy research or engineering. Experience in Interferometric Imaging and Calibration. Experience with Python package development and deployment. Experience with GPU application