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at the Department of Biosystems Science and Engineering (D-BSSE) of the ETH Zurich in Basel invites exceptional candidates to apply for PhD and postdoctoral positions in pioneering projects developing synthetic
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vehicles. Another research focus is on solid-state pulse modulators for medical applications (computer tomography/cancer treatment) and accelerators (CERN). For the design and optimisation of the various
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informatics, especially chronic disease management (with emphasis on asthma) Machine learning / deep learning / artificial intelligence Profile Ideally, the candidate has a degree in Electrical Engineering and
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of this project will bring forward the integration of novel methods at the intersection of advanced control, optimization, manufacturing science, robotics, and machine learning. The doctoral student position we
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with the lab’s objectives. Support the next generation of scientists (PhD students, postdocs), fostering a collaborative and productive research environment. Key areas of support include design analysis
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reports Profile MSc or PhD in Earth Sciences or closely related field Proven advanced expertise in 3D geological modelling Experience in managing diverse geological datasets (e.g., GIS data, 3D meshes
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understanding and practical experience with machine learning approaches for biomarker discovery and predictive modeling, specifically with hands-on experience in developing and applying neuronal networks
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that integrate machine learning with energy system optimization to make such policy optimization possible. The analysis will include policies for EV adoption, EV charging infrastructure, and electricity pricing
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the admission requirements for a PhD at ETH Zurich Experience in machine learning, optimization, or AI-driven decision-making Preferably with knowledge of Bayesian optimization or Gaussian processes
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Alps documentation of the technical and scientific aspects regarding the geological model(s) in peer-reviewed papers and technical reports Profile MSc or PhD in Earth Sciences or closely related field