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datasets and sophisticated modelling techniques to evaluate and optimize energy systems. We have developed in-house tools and utilize open-source datasets to support our research activities in various
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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
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approaches toward sustainable dairy farming, leveraging advanced statistical modeling, machine learning, and AI to uncover biological insights and optimize production efficiency, animal health and
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, precision livestock farming, and multi-omics approaches toward sustainable dairy farming, leveraging advanced statistical modeling, machine learning, and AI to uncover biological insights and optimize
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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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the Multiphase Fluid Dyamics group (Prof. Supponen) has a focus on modelling and high-speed imaging to optimize bubble generation and control, and the resulting particle dynamics and interaction with cells using
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. The optimal exploitation of digital tools in system development processes facilitates an effective and efficient process. To effectively capitalise on the existing knowledge base, digital tools, e.g., large
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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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hands-on learning experiences or with computational elements relevant to Artificial Intelligence or Optimization, in addition to running your research group. Since this is a leadership position, your
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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