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image processing techniques and modern machine learning approaches to extract meaningful quantitative information from complex biological images. The successful candidate will contribute to projects
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or similar tools) Initial experience with machine learning, clustering methods or generative AI (preferred but not required) Willingness and ability to collaborate with researchers from different backgrounds
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velocity changes at selected locations with the introduction of unsupervised machine learning and study the interaction of mass balance changes (crustal stress changes) and geohazards such as rain-induced
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PhD in Computational Materials Physics or a related area is required. Experience with electronic structure calculations is essential. Familiarity with the use of machine-learning tools in materials
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100%, Zurich, fixed-term Applications are invited for a PhD position in the Air Quality and Particle Technology group (Prof. Dr. J. Wang). The successful candidate will work in the Laboratory
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. The position is funded for up to two years. Salary and social benefits are provided according to ETH Zurich rules. Profile Applicants must hold a PhD or doctoral degree with a strong proven research background
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strong willingness to learn A solid foundation in experimental research, data analysis, and scientific methods Interest in machine learning and data-driven approaches to materials discovery Strong interest
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scienceEducation LevelMaster Degree or equivalent Skills/Qualifications Required Skills: Strong analytical background Proficiency in geometric deep learning and machine learning Prior experience in physics-informed
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responsible for ensuring the smooth operation of the IT infrastructure and services at NEXUS. Your responsibilities are: Core Responsibilities: Setup and manage virtual machines within ETH Zurich’s
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Apply machine learning techniques for data analysis and time-series forecasting Collaborate in a multidisciplinary team to accelerate functional thin film development Your work will be part of a larger