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vor 1 Woche


Geneva, Schweiz CERN Vollzeit

Job DescriptionIn this role, you will help develop advanced methods to compress deep learning models using low-rank and tensor-network techniques. You will contribute to the pQuant open-source library, working with an interdisciplinary team to make AI models more efficient and scalable, conducting research and development activity as part of a heterogenous team of physicists and computer scientists. Your responsibilities: Research and implement tensor-network methods for compressing neural networks. Explore low-rank approximations and their combination with knowledge-distillation techniques. Test and compare compression approaches on realistic datasets and applications. Contribute to the development, documentation, and maintenance of the pQuant open-source library. Work in an international, interdisciplinary team of scientists and software engineers. Share research outcomes through publications, presentations, and open-source contributions. This role includes team supervision responsibilities. Your profile: Expertise in deep learning, tensor networks. Experience in model compression techniques (knowledge distillation, pruning, quantization), FPGA programming, or hardware-aware AI deployment is highly desirable. Skills: Advanced knowledge of Linear Algebra. Basic programming skills (Python). Experience with Machine Learning. Spoken and written English or French, with a commitment to learn the basics of the other language. Eligibility criteria: You are a national of a CERN Member or Associate Member State . You have a professional background in Mathematics, Physics (or a related field) and have either:a Master's degree with 2 to 6 years of post-graduation professional experience; or a PhD with no more than 3 years of post-graduation professional experience. You have never had a CERN fellow or graduate contract before. Additional InformationJob closing date: at 23:59 CET. Contract duration: 24 months, with a possible extension up to 36 months maximum. Working hours: 40 hours per week Job flexibility: Hybrid Target start date: 01-February-2026 This position involves: Work during nights, Sundays and official holidays, when required by the needs of the Organization. Job reference: EP-SFT- -GRAP Field of work: Data Science & Data Analytics Benchmark job: - Applied Physicist What we offer A monthly stipend ranging between 6287  and 6911  Swiss Francs per month (net of tax) . Coverage by CERN's comprehensive health scheme (for yourself, your spouse and children), and membership of the CERN Pension Fund . Depending on your individual circumstances: installation grant; family, child and infant allowances;


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