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invites applications for a fully funded PhD position at ETH Zürich at the intersection of robotics, wearable sensing, signal processing, machine learning, and human-computer interaction. The goal of
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project aims to develop new methods for estimating blood pressure and quantifying flow inefficiencies using medical imaging, machine learning, and computational modelling. The doctoral candidate will
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both ETH Zurich and IBM Research Zurich, benefiting from the synergy between academic and industrial research. The topic of the PhD position sits at the crossroads of quantum computing, machine
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. By bridging machine learning, behavioural science, and clinical research, the project seeks to establish foundational methods for trustworthy agentic AI systems that can be deployed across diverse
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, indicators and survey information. The division combines macroeconometric modelling with data science methods for nowcasting, high-frequency indicators construction, machine learning, time series
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-informed neural networks (PINNs) and other machine-learning approaches build and extend our simulation environment, with a focus on performance, scalability and reproducibility apply your methods to real
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developing and applying digital tools in neurorehabilitation. Hands-on experience in machine learning techniques: theoretical foundations (e.g., model evaluation, data pipelines) and/or practical
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and high-resolution mapping methods with advanced machine learning feature detection to enable evolving subsea scenes to be robustly aligned across repeat surveys. You will develop probabilistic models
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research topics and demonstrate strong motivation. We are particularly interested in applicants with a strong foundation in machine learning/deep learning and medical image processing, excellent Python
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Übersicht PhD opening in 3D printed stone for heritage buildings 100%, Zurich, fixed-term Drucken The Chair for Digital Building Technologies invites applications for a PhD position on the 3D