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Field
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to find ways to increase profits, and they might do this in ways we didn’t anticipate. This project is about investigating what kind of market equilibria emerge from these multi-agent market interactions
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Your Job: Develop AI pipelines that translate -omic signatures into dynamic model parameters Implement reinforcement-learning agents that optimise model performance Collaborate closely with
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experience with spatial data analysis using GIS tools or spatial libraries and familiarity with common land-use datasets would be advantageous familiarity with environmental or agricultural economics research
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infectious diseases such as gastrointestinal (GI) infections, respiratory infections and sore throats. Using GI infections as a case study, the project will compare trends in OTC medication sales to other
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professor, one assistant professor, one postdoc, and a large number of PhD students. The project includes strong partnerships with the University of Leipzig (Bioinformatics, Prof. Peter Stadler
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knowledge in plant species identification. The successful candidate should be able to independently conduct statistical analyses in R and hold a valid driver's license. Experience in GIS and handling large
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to understand behaviour through the lens of digital footprint/“smart data” datasets, cutting across sectors ranging through health, retail, mobility, energy and communications. Using GI infections as a case study
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desired. Knowledge on statistical methods and their application is an extra merit. Good knowledge in GIS and R is a merit. Proven excellence in written and spoken English is essential. The fieldwork will
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scientists, cell biologists, bioimaging specialists and physicists, as well as a postdoc with a specific background pitcher plant development, transcriptomics and bioinformatics. Supported by this expert team
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research group with a broad interest in plant biomechanics, ecology, development and evolution. A supervisory team comprising a plant scientist, a cell biologist and a physicist, as well as two postdocs with