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northern Sweden create enormous opportunities and complex challenges. For Umeå University, conducting research about – and in the middle of – a society in transition is key. We also take pride in delivering
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
, computer vision, and data analysis using industry-standard tools such as Python, MATLAB, and deep learning frameworks. The student will enhance their ability to manage complex, interdisciplinary research
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to gain insights into complex systems and inform decision-making Design and evaluate scenarios that focus on energy systems and structural change, assessing their potential impact Develop and evaluate
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related field, and a strong interest in biomedical applications. Experience in machine learning, statistics, or high-dimensional biological data analysis is advantageous. Ideal candidates are curious
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research ideas, and to have solid mathematical foundations, such as in linear algebra and relational algebra, to support the design and analysis of your algorithms. You should be curious, motivated, able
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 3 months ago
5 Feb 2026 Job Information Organisation/Company Inria, the French national research institute for the digital sciences Research Field Computer science Mathematics Researcher Profile First Stage
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. Advanced training modules: Choose from practical courses and lecture series on topics like image analysis, microscopy, biostatistics, developmental biology, and more. Community & collaboration: Take part in
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optical devices (e.g. carrier dynamics, optical gain, waveguide optics) Ability to simplify complex physical behaviours to the core measurable parameters for efficient mathematical models. An affinity
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groundwater modelling, adaptive management, quantification of submarine groundwater discharge, and the analysis of nutrient transport processes. The PhD projects provide opportunities to engage in combinations
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on the analysis of complex event history data, with some relevant topics being the analysis of outcomes under competing risks, studies of individual heterogeneity, estimation using machine learning methods and