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detect reliable traces of life (biosignatures) – whether through remote sensing or direct onsite measurements – requires significant innovation. These challenges demand close collaboration across physics
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environments. The project will explore how learning-based and agentic AI systems (e.g., agents built on large language or foundation models) can be combined with physics-informed process models, digital
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mathematics, computational neuroscience, robotics, physics or a closely related field; Strong publication record in relevant venues; Solid programming skills in Python and a modern deep-learning framework
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use of solar energy. https://www.epfl.ch/labs/sci-enac-as/ Main duties and responsibilities As a PhD Student, you will be expected to: Assume full responsibility for your PhD dissertation; Perform
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18,500 people, including over 14,000 students and 4,000 researchers from more than 120 different countries. Postdoc in ML-Based Co-Design at EPFL Mission The EPFL computer vision lab ( https
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composites, ceramics, and metals (Composites: https://doi.org/10.1016/j.matt.2026.102809 , https://doi.org/10.1021/acsmaterialslett.5c00407 ;Ceramics/Metals: https://doi.org/10.1002/adma.202504951
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, including physics-based modeling across scales (discrete/mesoscale/continuum), thermodynamics-informed machine learning and neurosymbolic AI. They will also have the opportunity to lead international
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– more information can be found on our website (https://www.epfl.ch/campus/services/human-resources/en/basic-starting-salary-of-doctoral-assistants-and-postdocs/) Opportunity to perform state-of-the-art
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interdisciplinary research group A stimulating and international working environment Competitive salary and excellent working conditions – more information can be found on our website ( https
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admitted to an EPFL doctoral school program. For this position, candidates should also apply to the EPFL EDMT Doctoral School : https://www.epfl.ch/education/phd/edmt-management-of-technology/edmt-how-to