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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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protein systems. These datasets will, in turn, be used to train machine learning models capable of predicting mechanical behaviors of molecular and cellular systems. As a proof of concept, we will apply
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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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in physics, mathematics, applied mathematics, computer science, quantum computing, machine learning Strong interest in the development of theoretical or computational methods for optimization methods
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that makes a machine measurable. You will own a system from CAD through fabrication to hardware that still runs six months later If your focus is learning, you will develop policies and perception
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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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, and cell permeability. Job description The project will involve molecular dynamics (MD) simulations including enhanced sampling techniques as well as machine learning. Profile Applicants should hold a
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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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background You will contribute to the design and implementation of machine-learning-based sparse 3D image reconstruction modalities to be used for fast histopathological assessment of tumor biopsies with X
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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