24 data-"https:"-"https:"-"https:"-"https:"-"J" Postdoctoral positions at Aarhus University
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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will
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be developed and implemented in the GEOS-Chem chemical transport model, coupled to the Community Earth System Model. Standardized large wildfire events will be simulated based on historical data and
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graph algorithms for optimization under physical constraints Applying graph mining and graph data management techniques Designing computational methods for waste heat reuse and green transition goals
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biogeochemical modelling and data-driven machine learning approaches at an ecosystem scale to improve our understanding of the fate of nitrogen fertilizers applied to agricultural soils. This understanding will be
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measurements A good understanding of advanced physiological techniques Experience with enzymatic in vitro assays and plant x climate interactions Experience in complex data handling and statistical analysis
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and applying genetic and genomic approaches to biodiversity research. This includes integrating environmental DNA (eDNA) and molecular tools with ecological data to enhance our ability to assess
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environment focusing on integrating multi-source satellite remote sensing data and developing novel algorithms to quantify agroecosystem variables for environmental sustainability. You will focus on processing
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formulating and executing own research projects. Applicants should submit: Letter of interest PhD diploma. CV Recommendation letter Contact information For further information about the position, please contact
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Work The place of work is Ny Munkegade 120, 8000 Aarhus C. Contact Information Further information about the position may be obtained from / For further information please contact: Dr Simon Wall +45
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practice, this means leading groups composed of a few students in different projects and includes guiding them during experiments, data analysis and in the writing of research documents. You are also