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Prof. Livia Schubiger. The candidate will work with the IRDS group on projects that leverage NLP, causal inference, and machine learning to explore norms related to gender-based and other forms
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) for engineering systems. Our research covers surrogate modeling, reliability analysis, sensitivity analysis, optimization under uncertainty, and Bayesian calibration. We are known for developing the UQLab software
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recovery trajectories and injury patterns. Integrate personalized physiological measurements into a recovery prediction model, while adapting Bayesian Neural Networks for SCI data and analyzing the impact on
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, functional genomics, protein engineering, and targeted protein degradation. Project background High-throughput perturbation technologies rely on perturbing DNA or RNA to infer the function of proteins. Methods
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the Swiss Federal Railways via the ETH Mobility Initiative, will assess the ability to improve processes for rail system development to unleash efficiency gains, with specific examples for the cargo sector
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the EU Clinical Trial Regulation (CTR), managing all submissions and updates through CTIS. Serve as a key liaison with EU regulatory authorities, track evolving regulatory requirements, and ensure audit
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We look forward to receiving your application with the following documents as a single PDF: A cover letter indicating which track you are applying for (RL/Optimization, LLM/Knowledge or both) CV
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track. We then develop and evaluate a robust cannabis-driving detection model before and while driving. Ultimately, we are working towards a scalable digital biomarker platform for in-vehicle driver state
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, minutes, communication, documentation, cost tracking). In order to fulfil these tasks, it is essential to closely collaborate with all the project partners within ETH Library and at the other Swiss HEIs
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to changing conditions over the coming decades (e.g., when and where to expand rail, road, or cycling infrastructure, or add new transport services). Job description This doctorate aims to advance