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, sensors and instrumentation. Knowledge of one or more of the following areas:Power electronics, Reliability and failure mechanisms, Sensors and instrumentation, Data analytics, machine learning, or AI
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and sustainabilityIn line with our values , ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which
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are excited about future wireless and satellite communication systems, we encourage you to apply—even if you do not meet every item in the project description. We value candidates who are eager to learn
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courses in HDS domains to learn advanced health data sciences methods that they will later apply in a practical research setting for their individual research projects. Research: Interactive seminars and
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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High
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moisture risks, directly affecting children’s health, wellbeing, and learning outcomes. This PhD will develop evidence-based, Passive House–informed retrofit strategies tailored to diverse school typologies
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into the sample of interest. Recently we have been using AI and machine learning to predict the distortion present and significantly speed up this correction process. This PhD project will take the latest in AI
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are of growing economic and societal importance. By participating in a number of externally funded projects, doctoral candidates learn to perform independent scientific work. Through active participation in
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cybersecurity research. Who you are: You have BS in machine learning, cybersecurity, statistics, or related discipline with eight (8) years of experience; OR MS in the same fields with five (5) years
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Traditional Owners of the unceded lands on which we work, learn and live. We pay respect to Elders past, present and future, and acknowledge the importance of Indigenous knowledge in the Academy. Read about our