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Chemical Senses Training Program, and the Michigan Neuroscience Institute, which together provide a highly interactive training environment with excellent opportunities for collaboration and professional
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Inverse Methods and Ionospheric Modelling Research Fellow - School of Engineering - 106995 - Grade 7
empirical modelling, D-Region ionospheric remote sensing, or ionospheric radio propagation. This 3-year post will provide the candidate with the opportunity to fully engage with the research group, with work
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on the design and development of mechanically intelligent surgical tools that simplify surgical motions and incorporate image sensing. These tools will be integrated with robotic platforms and machine learning
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data analysis or remote sensing Have familiarity with planetary science datasets and/or space mission data Demonstrate analytical and critical thinking, as well as problem-solving Be able to work
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. Experience with land-surface models, micro-meteorology, Earth system modeling, and/or satellite remote sensing is an advantage. Experience with fieldwork in challenging environments is an advantage. Language
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skills in English Desired qualifications: Have knowledge of atmospheric and/or space physics Have experience with spectroscopic data analysis or remote sensing Have familiarity with planetary science
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Sensing (DAS) data processing and compression using ML Physics-driven machine learning for geophysical modeling and inversion Thus, the candidate is expected to have or about to have a PhD in a relevant
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for remote sensing and uncertainty estimation. Candidates must have a strong programming background. Requirements: PhD in Computer Science or a related field with a strong emphasis on machine learning
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in the health sciences, including fields such as healthcare informatics, movement and rehabilitation sciences, medical imaging, remote sensing, computer vision, mental health, data fusion