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than 18,500 people, including over 14,000 students and 4,000 researchers from more than 120 different countries. PostDoc Position at EPFL on Multimodal Foundation Models in Collaboration with Novartis General
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, protein purification, and/or machine learning is a clear plus. • Experience in data analysis and coding (preferably Python) • A systematic, independent working style and ability to work in a collaborative
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-learning models on resource-constrained hardware Candidates should also have experience or strong interest in Edge AI, TinyML, embedded machine learning, neuromorphic computing, or signal processing. We
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for personal and professional development Close collaboration between Empa and EPFL, bringing together complementary expertise in energy systems, machine learning, and foundation models The opportunity to shape
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medical oncology - Strengthen interdisciplinary collaboration between clinical and computational disciplines - Acquire competitive third-party funding - Contribute to undergraduate and graduate teaching and
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provable guarantees AI-based cybersecurity: applying learning and AI-assisted techniques to network security, e.g., automata learning from security logs, validation of protocol models, and verified defensive
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laboratory work, good organizational skills and the ability to work independently. Applicants should enjoy learning new methods and working in a collaborative research environment. Enthusiasm for infection
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industrial collaborations. ' Track record of publications in leading international journals. ' Must be fluent in English and willing to learn French. In your application, please refer to myScience and
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equitable energy systems. This PostDoc position is offered in collaboration with the Intelligent Maintenance and Operations Systems (IMOS) Laboratory at EPFL (Prof. Olga Fink ). IMOS develops advanced
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move beyond conventional stimuli-responsive materials, which merely switch between predetermined states, toward life-like materials that genuinely learn: Forming memories, consolidating them over time