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to the analysis of time series. In particular, the project will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show
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, natural hazards management or related fields Interested in protective forests and their management Good quantitative skills (e.g., data analysis, simulation modelling, remote sensing) Good communication
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myriad of new techniques and much sought-after skills demanded by industry, including the following: Design of experiments, data harvesting/analysis and interpretation of results to derive insight
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interdisciplinary team working on the development of cross-platform alignment technologies that integrate material science, process engineering and sustainability analysis to deliver scalable solutions for circular
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2026. The UNFoLD lab specialises in the experimental measurements, analysis, and modelling of unsteady vortex-dominated flow phenomena, with applications in bio-inspired propulsion, wind turbine rotor
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welcome) Entgeltgruppe 13 TV-L/75% to be filled. Starting date is 10/1/2025. The position is for three years. The positions are advertised as part of the Research Training Group 2491 "Fourier Analysis and
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synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application to the analysis of time series. In particular, the project
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data integration, cancer systems biology, and single-cell analysis, with strong links to clinical and translational oncology. Our work is based in the dynamic research environment of Heidelberg
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statistical models (for example principal component analysis) to obtain insights into relationships between physical properties of polysaccharides (composition, molecular weight charge, chain length etcetera
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analysis (R, Python) is an asset. Curiosity, creativity, rigor, willingness to learn, team spirit and collaborative capacity, excellent time and priority management. Fluency in English (written and spoken