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profile You have a master’s degree in computer science. You have experience with the humanitarian sector and/or in remote sensing. You are proficient in Python coding and deep learning frameworks. You are
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Mission The Chair of Operations, Economics and Strategy (OES) at the College of Management of Technology at EPFL invites applications for a full-time PhD student position within the EPFL doctoral
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PhD Student Position in Interoperable Digital Workflows for Regenerative Building Design and Perform
), Life-Cycle Assessment (LCA), embodied and operational carbon, circularity and material-reuse indicators, building monitoring, sensor data, Post-Occupancy Evaluation (PoE), and feedback from building
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. The goal will be achieved by establishing a network of 14 Doctoral Candidates (DC) working in synergy on complementary topics and supported by highly qualified supervisors from European universities and
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. The goal will be achieved by establishing a network of 14 Doctoral Candidates (DC) working in synergy on complementary topics and supported by highly qualified supervisors from European universities and
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. The goal will be achieved by establishing a network of 14 Doctoral Candidates (DC) working in synergy on complementary topics and supported by highly qualified supervisors from European universities and
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Physics, Optics, Micro-/Nanotechnology, Materials Science, Energy, or a related quantitative field A strong background in several of the following fields: theory of light-matter interaction, computer
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(20%). Profile An excellent MSc degree in Computer Science, Energy Science, Social Sciences, Environmental Sciences, Sustainability Science, Geography, or a closely related interdiscipline. A strong
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, the Lausanne center for ultrafast science, and the Quantum Science Center at EPFL Competitive salary, coverage of conference costs, a dynamic and inspiring scientific community. Informations The
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background, and an outstanding MSc degree in Engineering, Computer Science, Physics, Applied Mathematics, or a related field. You should be proficient in or willing to learn generative deep learning – in