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external funding and developing synergies with interdisciplinary expertise, methods, models, data and infrastructure at the department, the profile area Nature-based Future Solutions and the strategic
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, with a focus on handling non-determinism, model uncertainty, and changing data distributions in both pre-deployment and runtime contexts. Investigating runtime monitoring, testing, and explainability
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designing and conducting research pertaining to the project and includes obtaining, analysing and developing ways to manage and store data acquired for the project. Further, the holder of this position will
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methods for optimized data analysis, Machine learning-based image segmentation of tomographic data (e.g., synchrotron X-ray microtomography), Design and use of autoencoders (VAEs, GANs), diffusion models
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. The research team focuses on developing novel methods to extract knowledge from data, modeling large-scale complex systems, and exploring new application areas in data science. Areas of interest include but
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modeling of water quality and quantity in agricultural systems. At the division, there are about 35 employees and around 20 PhD students. The current position is within the Remote Sensing and AI in Hydrology
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for the project, which may also draw on data from other experimental catchments and large-sample databases. About the position This work will be a central component of the project “Intensification of water in
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Project description Third-cycle subject: Computer Science PhD students will work on robot learning
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communication systems for 5G and 6G, channel modelling, channel characterization, radio-based positioning and sensing, and associated signal processing algorithms. Work duties Employment as an assistant professor
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6 Sep 2025 Job Information Organisation/Company Chalmers University of Technology Research Field Computer science » Other Engineering » Materials engineering Physics » Other Researcher Profile First