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Field
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. • Proficient communication skills. • Track record of conducting original research and publishing in peer-reviewed scientific journals. Preferred Qualifications • Experience in remote sensing or machine learning
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contract is based on § 2 WissZeitVG. Your Tasks: You will conduct research in the field of experimental atmospheric physics, with a focus on optical remote sensing of the atmosphere up to the edge of space
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. Approval of remote and hybrid work is not guaranteed regardless of work location. For additional information on remote work at Penn State, see Notice to Out of State Applicants . This is a term position
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methods Supervising a small team of PhD and MSc students Developing new foundations of augmentations for digital pathology Potentially developing multi-scale AI models for cancer patient stratification
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innovation, and looking to make a difference. If this sounds like you, you've come to the right place! Responsibilities: Conduct original research on joint communications and radar sensing for spaceborne
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Phenomic prediction programs that incorporates genotype (genotype by sequence) and phenotype data (from traditional and high throughput, remote sensing sources) -Use or develop R programming code to complete
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collaboration with the division of Geoscience and Remote Sensing at Chalmers. The project is funded by the European Research Council (ERC) under the MIXCLOUDS project, with the following objectives: -Advance our
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that shape our future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a
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funded by the Grains Research & Development Corporation (GRDC). The project is led by CSIRO with the University of Sydney leading the component on the assessment of soil health using remote sensing
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communication skills. • Track record of conducting original research and publishing in peer-reviewed scientific journals. Preferred Qualifications • Experience in remote sensing or machine learning applications