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statistical and quantitative modelling. The successful candidate will contribute to projects on flow-ecology relationships and habitat suitability modelling for freshwater organisms and will be comfortable
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degree in the scientific area of Biology, Ecology, Environment, Natural and/or Forest Resources, Nature Conservation or related fields, obtained within the three years prior to the start date of the grant
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conditions 1. Scientific Area: Archaeology, Primatology, Primate Archaeology 2. Admission Requirements: - Masters degree in Biological Sciences with a specific focus on Ecology and Evolution - Bachelors
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 17 days ago
reducing supply chain vulnerabilities of critical materials and promoting proximity strategies, reuse, and recycling. To achieve these goals, the work will focus on developing stock-flow models for selected
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? Not funded by a EU programme Reference Number AE2025-0414 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt
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connectivity. You will also be in an excellent position to use your expertise in geospatial data analysis, statistical modelling, and/or ecological modelling to produce high-impact, open-access peer-reviewed
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research team members to ensure all project deliverables are met. Identify, simulate, and analyse ecosystem service models using GIS, remote sensing, and field data. Manage, process, and analyse ecological
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migratory species of seabirds, fish and marine mammals. In collaboration with the BioProtect project, the ecological connectivity models will feed into area-based decision tools for reducing pressures
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invites applications for a postdoctoral position in terrestrial ecosystem dynamics in the laboratory of Professor Paul Moorcroft http://www.oeb.harvard.edu/faculty/moorcroft/ . The successful candidate will
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Biodiversity, Genetics and Evolution Work plan: The fellowship holder will focus on developing and applying innovative ecological modelling approaches to improve predictions of species distributions under global