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camera system for acquiring plant seedlings in the visible spectrumDesign and programming of computer vision software to extract plant phenotypic dataCatalyze the development of the system from prototype
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camera system for acquiring plant seedlings in the visible spectrum Design and programming of computer vision software to extract plant phenotypic data Catalyze the development of the system from prototype
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evolution, polyploidy, and algal biology and evolution. The candidate will join a highly collaborative and international research environment, with strong connections to major polyploidy initiatives
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ideas, liaising with Center staff for the development of stories in support of central initiatives, and by reinforcing centrally developed stories via Center channels; You work closely with the HQ team
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hands-on responsibility to the development, implementation and continuous improvement of the data solutions and data products delivered by the team. As part of a collaborative team, you build and improve
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trainings, conferences, summer schools and personal development initiatives, tailored to the needs of scientists across the globe. About the role As a Training and Conferences Officer within the Training
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-on responsibility to the development, implementation and continuous improvement of the data solutions and data products delivered by the team. As part of a collaborative team, you build and improve these solutions
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evolution, polyploidy, and algal biology and evolution. The candidate will join a highly collaborative and international research environment, with strong connections to major polyploidy initiatives
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mathematical models to address fundamental questions in biology. Examples of research topics include but are not limited to: development of new AI architectures for biology and hybrid models that combine deep
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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
but are not limited to: development of new AI architectures for biology and hybrid models that combine deep learning with mechanistic models; foundation models of genome regulation using single-cell and