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100%, Zurich, fixed-term We are seeking a skilled Machine Learning Engineer to join our dynamic team. The ideal candidate will be involved in the development, optimization, and maintenance of our
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Network Architectures based on the available system resources (e.g., communication, computation, energy). Communication-efficient knowledge exchange among networked federated large models. These research
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(IVT) at ETH Zurich intends to develop scalable optimization systems for operational support in large-scale road networks. Modeling and simulation are powerful tools for the development and validation
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, multi-energy grids, and the integration of renewable energy and storage systems Your tasks In the framework of MSCA Doctoral Network (DN) Consumer Energy Demand Flexibility in Electricity Use (CoDEF
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, conceptualizing and bringing projects to life You have demonstrable experience with digital tools (e.g. Docker, networking, Unix, Windows) You’re proficient in at least one programming language (e.g. Python
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of extension based on funding. Project background Scenarios for the future energy system provide insights for decision-makers on pathways to achieving net-zero emissions. These scenarios are developed by
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research in an interdisciplinary and international team, and strive for scientific excellence; Excellent communication abilities, social skills, and independent attitude paired with the ability to network
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, interactive content, and technology-driven teaching approaches. Provide hands-on support to faculty members—including new and external lecturers—by optimizing existing courses and developing new ones, ensuring
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validate predictive algorithms for biomarker discovery Optimize data integration techniques for multi-omics and clinical datasets Perform trend analysis of bacteria-containing samples over time to observe
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of Transformable Sites: Using UBEM to pinpoint optimal locations for densification based on spatial, architectural, and socio-economic assessment Implementation and Environmental Assessment: Applying densification