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24.02.2026 Application deadline : 06.04.2026 The Faculty of Science at Tübingen University invites applications for a Tenure-Track Professorship (W1 to W3) in Evolutionary Modelling (m/f/d
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for Applied Marine Ecology and Modelling (for more information see: https://ecos.au.dk/en/researchconsultancy/research-areas/applied-marine-ecology-and-modelling ). The department is, and wishes to continue to
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modelling in transdisciplinary processes in order to examine ecological and socio-spatial change, existing biodiversity governance regimes and develop multispecies ethnographies to better understand
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underwater acoustics and marine ecology. CMST pioneers innovative methods to monitor and manage marine environments. We measure, monitor, model, and predict anthropogenic noise. We are experts in sound
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restoration ecology (see https://www.slu.se/en/about-slu/organisation/departments/department-of-wildlife-fish-and-environmental-studies/ ). The department has many international employees and well-established
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C3 /C4 transitions occurred in the past Publish results in scientific journals with highest scientific standards Your Profile Master or PhD degree in Biology, Plant Ecology, Environmental Modelling
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computational models in pandemic science. These models can be applied in any relevant field! Sociology, Computer Science, Psychology, Game Theory, Immunology, Physics, and/or Ecology. Yes! Any/all of them! We
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of Microbial Ecology and Biogeography (https://kohoutlab.com/ ), is seeking a motivated PhD student to join the consortium of the Excellence Research Centre project INTER-MICRO, which aims to identify how
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French National Research Institute for Agriculture, Food, and the Environment (INRAE) | Le Rheu, Bretagne | France | 2 months ago
well as on the complementary expertise of our collaborative network (reverse chemical ecology, formulation of odour compounds, modelling of odour plumes, etc.). You will be involved in supervising trainees and doctoral students
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Ph.D. in ecology, evolutionary biology, statistics or a related field. Experience with field work and field experiments with plants Experience with statistical modeling and basic statistical concepts