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Creation date"Creation date" is a predefined property that corresponds to the date of the first revision of a subject and is provided by <a target="_blank" rel="nofollow noreferrer noopener" class="external text" href="https://www.semantic-mediawiki.org/wiki/Help:Special_properties">Semantic MediaWiki</a>.
19:32:15, 6 October 2020 +
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October 30, 2020 +
'''At the Department of Physical Geography … '''At the Department of Physical Geography. Closing date: 30 October 2020.'''<br></br>The Department of Physical Geography is one of the major departments within the Faculty of Science. The department has approximately 135 employees and educates approximately 1 000 students annually. Education is oriented towards geography, geosciences, biology-earth sciences, and environmental protection and environmental management. The main research areas are: Biogeography and Geomatics, Climate Science and Quaternary Geology, Environment, resource dynamics and management, Geomorphology and Glaciology, and Hydrology, Water Resources and Permafrost.</br></br></br>'''Project description'''<br></br>The position will be associated with a project on physics-guided applications of Machine Learning (ML) methods for hydro-climatic and freshwater system dynamics on land. The project aims at seizing the enormous opportunities opened by rapid growth in openly available/accessible data and ML methods to systematically and significantly advance data-interpretation, modeling, and predictive capabilities for large-scale hydro-climatic and freshwater-resource conditions and shifts in various parts of the world’s land area up to the global scale. The approach to capturing these oportunitities will combine physics-based fundamental mechanistic constraints and models with relevant state-of-the-art ML methods. This combination is needed to leverage complementary knowledge and methodological strengths and evade false scientific discoveries that solely black-box use of ML often leads to in data-intensive exploration. Data to be considered range from in-situ measured, remotely sensed, and reanalysis data, as well as simulation outputs from Earth System and other types of large-scale models, such as from catchment/regional/global hydrological modelling. Hydro-climatic and freshwater-resource variables to be considered include, e.g., land-atmosphere flux interactions, soil-moisture and water flow/level/quality conditions, and climate-change and land/water-use drivers of possible shifts in these.</br></br>For more information see: https://www.su.se/english/about-the-university/work-at-su/available-jobs?rmpage=job&rmjob=13169&rmlang=UK-at-su/available-jobs?rmpage=job&rmjob=13169&rmlang=UK
Postdoctoral Fellow +
Postdoctoral Fellow in Physics-guided Applications of Machine Learning Methods for Hydro climatic and Freshwater System Dynamics +
Stockholm University +
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Finland +
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19:32:15, 6 October 2020 +
NO STATE +
