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Field
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: Experience in method development, working with spatial data, and GIS Experience with univariate and multivariate analysis and working with large datasets Experience with working independently and organizing
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developmental signaling pathways (Notch, Hippo) support tumorigenesis and might reveal novel therapeutic vulnerabilities. Every project incorporates the evaluation of novel pharmacologic agents to shepherd
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characterised at atomic resolution via crystal structures of PrfA–peptide complexes. Building on these findings, this PhD will develop peptide-based PrfA inhibitors as novel anti-Listeria agents. The project will
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implant failure and limb amputation. Current antimicrobial coatings often rely on antibiotics or metallic agents, which may contribute to antimicrobial resistance (AMR) or cytotoxicity. This project aims
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approximation algorithm, using linear programming methods. Over the next years, the project will grow to a collaborate team of 4-5 PhD students and Postdocs. The successful candidate will work directly with the
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, forestry, bioinformatics, or a related field - Strong demonstrated interest in biodiversity, molecular methods, or forest ecology - Advanced Skills in R/Python, GIS, bioinformatics, and molecular lab work
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management Nordic forestry Remote sensing data: ALS, TLS, satellite (e.g. sentinel2), aerial images Statistical modelling and analysis GIS e.g. ArcGis, Qgis, R Programming, e.g. R, Python, C etc. Field work
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methods, or forest ecology - Advanced Skills in R/Python, GIS, bioinformatics, and molecular lab work - Ability to work independently and in multidisciplinary teams - Strong English communication skills
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and Stata. Working with large datasets, including merging files into the data archive. Creating clear data visualizations to communicate results (including GIS maps). Assisting with the preparation
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abandoned buildings. A more qualitative approach will also be implemented, with surveys and interviews with property owners, policymakers, and sector agents, using text analysis tools. 5. Application