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Field
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learning, and computer graphics. The positions are fully-funded with payments and benefits according to German public service positions (TV-L E13, 100% for PhDs and TV-L E14, 100% for PostDocs; 45k – 57k
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learning, and computer graphics. The positions are fully-funded with payments and benefits according to German public service positions (TV-L E13, 100% for PhDs and TV-L E14, 100% for PostDocs; 45k – 57k
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vision, machine learning, and computer graphics. The positions are fully-funded with payments and benefits according to German public service positions (TV-L E13, 100% for PhDs and TV-L E14, 100
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to the 30th September 2026. We are looking for outstanding machine learning researcher to join the Torr Vision Group and work on AI Scientists: systems that use foundation models, AI agents, and robotics
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contribute to patents or technical innovations. Qualifications: PhD in Artificial Intelligence, Machine Learning, Data Science, Electrical Engineering, or a related field. Strong experience in developing and
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part-time post (0.2FTE) ideal for someone working in industry or with industry experience. This is because we want to bring in expertise with data processing and machine learning pipelines, and their
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or equivalent Skills/Qualifications - PhD in bioinformatics or related subjects - Expertise in python coding - Experience and good understanding of neural networks and machine learning - Fluent written and spoken
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, machine learning and surrogate modeling; and presentation skills and proven research independence. Application material should include a cover letter, CV, and the names and contact information of three
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Mattelaer, Christophe Ringeval). Research activities in include SM and BSM aspects of collider physics (LHC and future colliders, simulation tools, machine learning, effective field theories, amplitude
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FieldMathematicsYears of Research ExperienceNone Additional Information Eligibility criteria The position requires a PhD in machine learning, NLP, causality, or a related discipline, with a strong command of deep