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infrastructure, and GPU/CPU cluster environments. This role leads and mentors a team of Systems Engineers and Administrators while remaining deeply technical and hands-on, actively designing, deploying, and tuning
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GPU acceleration (CUDA) Participation in relevant competitions (e.g., Kaggle, computer vision challenges) Experience with version control (Git) and collaborative development practices
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using GPU-based software packages, custom-trained neural networks, and related tools. Analyze and interpret high-dimensional neural datasets using systems neuroscience approaches such as neural networks
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-based methods (such as SDM-UniPS or Uni-MS-PS). Activity 2: Implementation of the RNb-NeuS Module - Develop the neural reconstruction node using photometric data as input. - Ensure interfacing with
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facilities. Core responsibilities include the deployment and maintenance of small-scale HPC and compute nodes, GPU workstations, Linux and Windows servers, research data storage and backup solutions
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roles or positions in industry. A supportive and collaborative team committed to fostering your growth as a researcher. What You’ll Do Process calcium imaging data using GPU-based software packages
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scenarios Convolutional neural networks for computer vision require substantial computing resources and introduce significant latencies even in modern GPU systems. This project investigates neuromorphic
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or similar deep learning frameworks GPU know-how: Familiar with GPU workflows and distributed training setups Data competence: Experience in preprocessing, augmentation, and dataset organization; confident
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, scientific computing, etc). Strong scientific computing background, with experience of different architectures (e.g. CPUs/GPUs) and their use in high-performance computing through shared or distributed
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the necessary algorithms. You will also develop and document a graphical user interface which handles large processing tasks efficiently and uses multiprocessing and GPU acceleration where necessary. At the same