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on remote sensing of air quality. Emphasis will be placed on using satellite observations to understand the impacts of wildfires and prescribed burning on air quality. The candidatemust have a Ph.D. in a
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Parkinson’s disease patients was performed using novel and investigative sensing neurostimulators. Our team has deconstructed brain activity to discover both the neural code responsible and kinematic
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/Offerte_di_lavoro_FBK_is_looking_for_a_Researcher_i… Requirements Research FieldOtherEducation LevelPhD or equivalent Specific Requirements The ideal candidate must have the following requirements: PhD degree, preferably in the field
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water quality parameters and predict cyanobacteria blooms in the Tietê system reservoirs. Activities: 1. Develop machine learning models for estimating water quality parameters via remote sensing; 2
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of a PhD in epidemiology or public health related field Demonstrated success in implementing innovative community-based interventions. Track record of publications in peer-reviewed journals, preferably
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Are you passionate about integrating agronomic, genomic, and remote sensing data to better understand plant stress, disease progression, and crop resilience? Do you enjoy working across field campaigns
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of scientific manuscripts, reports to funders and other materials for other audiences based on the results from research studies. You must hold a PhD/DPhil (or be near completion) in epidemiology, public health
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the fields of Artificial Intelligence (AI) and Earth Observation (EO). It has extensive experience in remote sensing, particularly in radar imaging and its application for measuring surface displacement. It
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existing national datasets (including remotely sensed data) and will conduct a mixed-methods data collection campaign that emphasizes mapping intervention-specific adoption, adoption outcomes and identifying
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catchment hydrology, remote sensing, or climate impact assessments. Candidates with interdisciplinary backgrounds are welcome, but strong data-analytics skills and solid knowledge of process-based numerical