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, or HCI methods familiarity with adaptive systems or machine learning prior experience conducting user studies Beneficial background in computational interaction or adaptive systes knowledge of optimization
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. Scientific IT Services (SIS - a section with ITS) aims at bridging the gap between computational research and IT services and infrastructure provisioning. We are working closely together with ETH researchers
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. The focus is on developing AI-supported, sensor-based solutions for real-time monitoring and optimization of manufacturing processes. These solutions aim to assess process conditions during production
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-transportation system, we are looking for a: PhD Student in Data-Driven Policy Optimization for Transportation and Energy (100%) Project background Our energy and transportation systems are rapidly transforming in
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the bacterial import process, optimizing the system for high performance, and then applying it to problems around therapeutic peptides and proteins. The focus of the work is experimental and will include a broad
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Intelligence in Mechanics and Manufacturing (AIMM) at ETH Zurich, is offering a position in the field of data-driven optimization. Project background Our research focuses on the development and application
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). An optimal background for the project would be Electrical Engineering bachelor's (sensing, signal processing) with a more Computer Science-oriented master's (machine learning, time series processing). This is
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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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Zurich is a community of approximately 50 researchers from more than 20 countries working on the development of methods and computational tools for automation, exploring their potential for promoting our
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experience in developing software for scientific applications, data analysis, or real-time systems is desirable. Experience with parallel computing and optimization techniques for handling large datasets