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Seppälä (University of Innsbruck, Austria). Postdoctoral Position Details You will be involved in the modelling component of the project. Responsibilities include: Modelling the ecosystem-level effects
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statistical methods such as dimensionality reduction and Bayesian modeling. This project offers access to a rich, curated clinical dataset and collaboration with leading neurologists, neurosurgeons, and data
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pollutants without leaving harmful residues. The most common method, surface dielectric barrier discharge (SDBD), uses micro-discharges to convert O₂ into ozone. Several key factors govern the discharge
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knowledge with data-driven methods. Physical knowledge includes, among others, kinematics and the dynamics of the robot, terrain interaction and contact models, and environmental physics, such as wind. The
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, to study various aspects of Fibroblast Growth Factor (FGF) signaling in disease. Current focus is on non-canonical FGF signaling pathways, differential signaling of FGF-receptors, and development
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programming and strong quantitative skills. Desirable Demonstrated knowledge of advanced biogeographic, comparative, and phylogenetic methods, quantitative methods in biodiversity studies, GIS in R, and spatial
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studies Application of basic and advanced statistical methods (e.g., linear/logistic regression, Cox regression, potentially also multilevel analysis, structural equation modelling, mixed-effects modelling
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) of a Doctoral Study Program (Ph.D.) in Chemistry, Biology, or a related field. Experience in the field of Polymer Chemistry, evidenced by a minimum of 10 publications in journals with an Impact Factor
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and chemometrics. The tasks include the development and application of spectroscopy-based methods as alternatives to traditional chemical analyses, spectral data processing and multivariate analysis
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carbon and nutrient dynamics in peatland ecosystems, with a strong focus on FTIR spectroscopy and chemometrics. The tasks include the development and application of spectroscopy-based methods as