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Overview of the Role Are you an ambitious researcher looking for your next challenge? Do you have an established background in hydrology and climate modelling? Are you looking to make a real-world
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application of innovative Machine Learning (ML) frameworks to understand and predict the global hydrological cycle. The role will require bridging the gap between process-based physical modeling and scalable
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-resolution measurements of topographical changes and soil loss issues within U.S. farmlands. This research will provide datasets for hillslope- to landscape-scale hydrology and erosion technology to limit
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level change, and to better understand hydrologic extremes like flood and drought. A postdoctoral fellow will participate in observation and/or modeling of the terrestrial water cycle. Generally, this
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responses to environmental disturbances. Fellows will practice a variety of data collection, monitoring, and analysis techniques by collaborating on the following research track: Post-Fire Hydrology and
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Research Fellow in Cloud Modelling Salary: Grade 7 (£41,064 – £48,822p.a. depending on experience) This role will be based on the university campus, with scope for it to be undertaken in a hybrid
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by 9/1/2026. Preferred skills: Experience in process-based crop, soil, or hydrological modeling. Proficiency in Python and other programming languages (Fortran and C/C++ are a plus). Familiarity with
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Do you have a strong technical background in Corrosion, Machine Learning and Numerical Modelling? Are you interested in working with industry to develop Machine Learning methodologies and protocols
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25 Apr 2026 Job Information Organisation/Company University College Dublin Research Field Engineering » Chemical engineering Chemistry » Biochemistry Researcher Profile Recognised Researcher (R2) Application Deadline 22 May 2026 - 01:23 (UTC) Country Ireland Type of Contract Temporary Job...
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Bio and Agro-Defense Facility (NBAF), the Arthropod-Borne Disease Research Unit (ABADRU), and the Geospatial and Environmental Epidemiology Research Unit (GEERU) to model the risk of highly pathogenic