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. This research builds on recent work on optimization-based workspace adaptation in VR/MR and aims to advance interactive systems that dynamically adapt to user behavior, preferences, and intent to optimally
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for further optimization in medicinal chemistry is a primary focus. We closely collaborate with our therapeutic areas and functions to convert hypotheses into innovative therapeutics. The opportunity We
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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 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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currently seeking to hire an HPC Optimization Specialist. The objectives of this position are twofold: on the one hand, to extend our HPC user support to help researchers to run their applications and HPC
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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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part of the Therapeutic Modalities function, where the discovery of chemical starting points for further optimization in medicinal chemistry is a primary focus. We closely collaborate with our
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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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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