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the project for an independent post-doc to develop their own ideas and sub-projects Ability and motivation to work as part of a team. Excellent written and verbal communication skills. The duties, qualification
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chemistry-based approaches for organic new-particle formation; Evaluating and advancing modelling capabilities of the PALM-SALSA model system in simulating urban air quality; Mapping changes urban air quality
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; Evaluating and advancing modelling capabilities of the PALM-SALSA model system in simulating urban air quality; Mapping changes urban air quality in selected cities. The applicant should Hold a doctoral or
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modeling (e.g., Rhino/Grasshopper, Phyton, Unity, Blender). Point cloud processing (e.g., CloudCompare, Autodesk ReCap) and/or GIS. Immersive simulation environments and interactive design tools. Experience
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. The position is funded by the Jane and Aatos Erkko foundation (Project GIN2) and is a fixed-term contract from January 2, 2026 to July 31, 2028. The post-doc researcher will work in a highly multidisciplinary
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Doctoral Researcher in statistical signal processing. The Structured and Stochastic Modeling Group, headed by Prof. Filip Elvander, conducts research in statistical signal processing, ranging from
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. Applying quantitative and modeling approaches (e.g., paleoclimate, landscape, or ecological niche modeling) to interpret ecosystem dynamics. Contributing to grant proposal preparation and co-authoring
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. This is an exciting opportunity to work on developing GaAs PICs in a project that includes everything from III-V laser epitaxy design and simulations, and fabrication, to system level PIC lidar tests and
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. Integrate environmental, spatial, and social data into digital twin models for scenario testing and policy simulation. Adapt co-design methods to local contexts in demonstrator sites (Portugal, Sweden, Italy
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-making. Team members bring complementary expertise, and by working together we address novel problems that no single approach could solve alone. Multimodal foundation models Key words: multimodal learning