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Position as Computational Analyst / Bioinformatician in RNA Therapeutics and Cardiometabolic Disease
of (micro)RNA biology, 3D human model systems, nanomedicine, and computational (ML) disease modelling. You will be able to contribute to our vision to translate basic findings into medicinal RNA approaches
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PhD in a relevant field (e.g. political science, statistics, computer science, informatics, economics, or related discipline) with a demonstrated focus on forecasting, statistical modelling, and/or
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-oriented events and contribute to strengthening its visibility and engagement with global research and innovation ecosystems. Qualifications Applicants must hold a PhD degree in electrical/electronics
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Statistics we conduct research within the theory and implementation of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale computational methods
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Requirements You are an independent, highly motivated researcher with solid interdisciplinary experience in agronomy and plant genetics, and a growing foundation in spatial data analysis. You also possess: A PhD
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Sami Spatial Planning through Participatory Design is to explore participatory design models that integrate Sami cultural values and knowledge into spatial planning. The project addresses the rapid
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, epidemiological, and environmental data Taking part in developing and validating predictive cancer‑risk models Contributing to spatial analysis and data integration in geographic information systems (GIS
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for establishing a research group dedicated to quantitatively assessing system-level implementation and market potentials of innovative climate solutions. This includes modeling climate solutions spatial and socio
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and chemical oceanography and the intersection of these disciplines. Potential research lines could focus on spatial and/or temporal aspects of e.g. ocean carbon cycling, physical oceanography, ocean
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. The research in the PhD project will focus on core spatio-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation