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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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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
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Tenure-track Assistant and Associate Professorship positions in Data Science and Machine Learning...
cryptography. We also offer: World-class facilities: Access to state-of-the-art local High-Performance Computing through CPU and GPU clusters at the SDU eScience Centre , as well as to national and European
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such as NumPy, SciPy, PyTorch or TensorFlow Experience with C/C++ and GPU/accelerator platforms is an asset Hands‑on experience with software‑defined radio platforms, RF measurement equipment, or laboratory
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-performance workstations, CPU/GPU clusters, and experimental systems tailored for fish and fly research. This role will necessarily involve both software development and software-hardware integration, with
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Post-doctoral Researcher in Multimodal Foundation Models for Brain Cancer & Neuro-degenerative Disea
translation models and medical images analysis are considered important assets. Established expertise in key machine & deep learning frameworks and toolsets. Experience in GPU computing, HPC, Containers & Image
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/ computer vision and pattern recognition, including but not limited to biomedical applications Strong interest in applied machine learning, including but not limited to deep learning Experience utilising GPU
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: An extensive GPU-based accelerated computing facility and high-performance data infrastructure is available to support lab activities. Engineering Support: A dedicated Engineering unit provides on-demand
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are looking for candidates with experience in ML model deployment, workflow orchestration, and high-throughput data processing, as well as experience working with large biological datasets in GPU-based
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computing capabilities with an in-house cluster serving 80 CPU cores and 1.5TB of RAM, as well as a newly acquired NVIDIA DGX box with eight H100 GPUs and 224 CPU cores. We analyze large public datasets