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PhD Scholarship - Energy-Efficient Decentralised Training Frameworks for Large-Scale AI Models on Geo-Distributed Infrastructure Job No.: 696617 Location: Clayton campus Employment Type: Full-time
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Qualifications · PhD or MD/PhD in neuroscience, molecular/cell biology, bioengineering, or a related field. · Demonstrated experience in extracellular vesicle exosome biology and advanced statistical analysis
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, Physics, Computer Science, Engineering, or related field. 2–5 years of relevant experience after PhD (industry or applied postdoctoral research). Expertise in quantum algorithms and NISQ computing
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audio algorithm code base including noise reduction, acoustic echo cancellation, automatic gain control, voice activity detection, packet loss compensation and other audio enhancements Supporting Research
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the content of the science they study. You will be an active member of RCNP. You will collaborate with the other postdoctoral researchers, PhD candidates and senior investigators, participate in seminars and
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involving Prof. Dr. Michael Bader (TUM CIT, Hardware-aware algorithms for HPC) , Prof. Dr. Felix Dietrich (TUM CIT, Physics-enhanced Machine Learning) , and Prof. Dr. Hartwig Anzt (TUM CIT, Computational
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, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: BS, MS or PhD
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and/or Windows environments Working knowledge of modern development tools (Git, CI/CD pipelines, automated testing frameworks) Knowledge of data structures, algorithms, and performance optimization
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maintaining software in Linux and/or Windows environments Working knowledge of modern development tools (Git, CI/CD pipelines, automated testing frameworks) Knowledge of data structures, algorithms, and
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maintaining software in Linux and/or Windows environments Working knowledge of modern development tools (Git, CI/CD pipelines, automated testing frameworks) Knowledge of data structures, algorithms, and