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Doctoral Candidate in computer vision and machine learning for developing novel deep learning method
Machine Learning (DM3L) Doctoral Candidate in computer vision and machine learning for developing novel deep learning methods for satellite-based tracking of global CO2 and NOX emissions of point sources 80
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and postdocs. In parallel, they will oversee the development and implementation of computational strategies to support and enhance research activities across the institute. Your Responsibilities: Lead
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(e.g., RNA-Seq, 5mC / 5hmC-Seq, DNA-Seq, ATAC-Seq, CUT&RUN / CUT&Tag, 16S) Experience in automated sample preparation, as well as project management skills to organize multiple parallel projects
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the production and processing of aluminum, magnesium, and titanium components and is a leader in the further development of additive manufacturing processes, particularly wire-based arc additive manufacturing, a
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project for the realization of laser modules for use in space, to start as soon as possible. Reference number 31/25 Your tasks Development and management of product assurance processes, including
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defining its strategic direction, processes, and services, according to the researchers’ evolving needs Advising researchers on the development of their external funding portfolio and monitoring new funding
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Our Corporate Health Management Program offers a holistic approach to your well-being APPLICATION PROCESS: Interested candidates should submit the following documents: A cover letter detailing their
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research in an international environment. Excellent documentation skills, computer literacy (Microsoft Office, Electronic lab book applications) as well as fluent English are required. We offer a stimulating
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Winterthurerstrasse 190, 8057 Zürich, Switzerland Further information Questions about the job Prof. Anastasia Koloskova Professor Questions about the application procedure Nicole Trolese HR Manager Working at UZH
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climate data and process-based hydrological and ecosystem models. Utilize semi-operational, high-resolution hydrological simulations on a supercomputing cluster at the national level. These simulations will