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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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(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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guidance and robotics. Our work combines medical imaging, computer vision, and machine learning with strong clinical translation, in close collaboration with Balgrist University Hospital and the national
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for publication in peer-reviewed journals Acquire third-party funding Possibility to initiate and pursue the habilitation process Support team members in the planning and execution of clinical research projects and
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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
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. Play a key role in the operational management of the program, including applications, student advising, enrollment processes, ongoing marketing activities and events, examination organization, and
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disease processes of human tissues and microbial entities. To use microbial effector and resilience mechanisms shaped by millions of years of microbial evolution as a blueprint for engineering human health
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, including applications, student advising, enrollment processes, ongoing marketing activities and events, examination organization, and serving as a contact person for students and stakeholders. Acquire and
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buried in the sediment. The aspects to be investigated here are the interaction of frequency-dependent propagation and imaging effects in conjunction with the signal processing with regard to image