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pipelines for reproducible, efficient and scalable training and inference on parallel, distributed and GPU-accelerated computing systems Benchmark the developed approaches against established methods
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<span lang="en">TUD Dresden University of Technology</span> | Dresden, Sachsen | Germany | about 21 hours ago
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- compiler frameworks (LLVM, MLIR)
- embedded software development
- computer and accelerator architectures
- parallel and distributed computing
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. Deep expertise in Linux and HPC architecture, including heterogeneous CPU/GPU compute, workload scheduling, high-speed interconnects, parallel or distributed file systems, cluster management, artificial
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, artificial intelligence, computer networks, graphics, parallel and distributed systems, mobile systems, machine learning, Cybersecurity and secure computing, data science and AI, and theory. Our faculty are
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Intelligence, Machine Learning, Data Science, Security and Privacy, Parallel and Distributed Computing, Deep Learning, Internet of Things (IoT), and Algorithms. Successful candidates will hold a
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(Docker, Kubernetes), and robust debugging techniques. Experience with cloud platforms (AWS, GCP, or Azure) and parallel computing, with a focus on cost-efficient and scalable model deployment. Skilled in
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quickly and functioning in a dynamic technological environment. Preferred Qualifications: Experience with parallel computing, GPU operation (CUDA Toolkit), multi-GPU training, and distributed
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(IEE) , the Chair of Highly-Parallel VLSI Systems and Neuro-Microelectronics offers a project position as Research Associate (m/f/x) Physics- and Quantum-Inspired Computing on the SpiNNaker2
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, prototypes, evaluates, and scales cutting-edge computing environments, automated workflows, and advanced intelligence applications. The unit focuses on shifting manual paradigms into optimized digital
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. About us The Department of Computer Science and Engineering , a joint department of Chalmers and the University of Gothenburg, spans the breadth of computing disciplines. Our internationally