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Finance at EPFL seeks postdocs to reinforce our research in quantitative finance with a focus on applied machine learning. Main duties and responsibilities Working and collaborating on research projects
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of Singapore, and EPFL (Switzerland). These partners are looking for talents in several domains of machine learning, AI, computational biology, and biology, to develop PhD theses across the main pillars
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and associated environmental impacts. Contribute to short-term (2026 to 2030) and long-term (2030 to 2050) verticalisation forecasting models based on machine learning, and to their validation against
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Understanding human motivation requires methods that go beyond questionnaires and simplified computer-based tasks. This project aims to develop more naturalistic, yet highly controlled, behavioral assays in which
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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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and in computer architecture domains, our ability to analyze data still falls behind the unstoppable data collection rates. Data-intensive applications are increasingly more demanding in sophisticated
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curriculum of entrepreneurship classes for science and engineering students, helping them to acquire entrepreneurial skills and explore venture creation alongside their studies. You will have the opportunity
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Modelling (BIM), Geographic Information Systems (GIS) or computer programming is a plus. A master's degree or professional experience in architecture is preferred but not required. The employment rate varies
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interest in contributing to both the conceptual and empirical dimensions of research Proficient in, or willing to learn, programming languages (e.g. MATLAB, Python, R) Experience with one or more of the