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» Remote sensing Researcher Profile Leading Researcher (R4) Established Researcher (R3) Country Netherlands Application Deadline 19 Oct 2025 - 21:59 (UTC) Type of Contract Temporary Job Status Not Applicable
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Position Description: Schedule: Monday-Friday 8:00am-5:00pm Location: Remote The Access Center Representative will have good communication skills, both written and verbal, along with good active
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to contribute to a highly collaborative, international project developing open-source low-field MRI systems for neuroscience research; Have research experience in compressed sensing and/or multi-contrast image
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impact and adaptation analyses, and for spatio-temporal modelling as well as upscaling of ecosystem properties via remote sensing Interactive collaboration and exchange within the TUM Center for Forest
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such as case weighting, anomaly detection, and model-based prediction (e.g., geostatistics and machine learning), using auxiliary geospatial or remotely sensed data. Quantifying uncertainty and correcting
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species, and the emergence of previously unseen classes. Recent advances in remote sensing and machine learning provide new opportunities to address these challenges, but most current approaches
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processes, remote sensing observations, and prediction and valuation of forest functions, providing a holistic view on forests as complex systems. See the project website to find out more about the FORFUS
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Job Summary The position involves leading a field crew over 12 months to locate and visit often remote field sites, sometimes in areas of recent wildfire burns, plus a month of office and laboratory
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USNH Employees should apply within Workday through the Jobs Hub app The Soil Frost Sensor Researcher will be responsible for building automated soil frost sensing systems using HOBO pressure
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with potential for a hybrid (on campus and remote) schedule in the future. All positions may require on campus meetings. Remote work is only considered for residents of MA, VT, NH, ME, CT, RI, or NY