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international conferences; assisting with activities to disseminate the project's findings both within and outside the academic world; publishing peer-reviewed articles and book chapters. Your qualities You are a
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we’re growing along with it. At the Universiteit Maastricht (UM), your work makes a difference—whether you explore complex biological systems, contribute to cutting-edge research, or help shape
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into answering counterfactual questions. Using remote sensing multimodal time-series data and Earth foundation model embeddings, you will design and develop causal machine learning models tailored for dynamic
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the preparation of new mission concepts as well as the development of new measurement technology. Objectives of the position This PhD project focuses on the effect of high-energy radiation on airplane altitudes
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1 Research fellowship for MSc (BI), to a PhD student or MSc enrolled in a non-academic degree course
enrolled in a non-academic degree course, within the Research Project entitled “Polymer-IR780 conjugate based Tetrazine-Norbornene click injectable in situ forming hydrogels: fine-tuning the polymer
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Agreement pay scale. Contract type: Technical and scientific activities contract linked to project activities. What can we offer? Incorporation into the Vall d’Hebron Research Institute (VHIR), a public
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, and vehicles collaborate. Your work assignments You will work in the ELLIIT funded project “Safety-Critical Intelligent Machines with Cloud-Assisted AI and Control”. This project focuses on safety
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priorities. This may include: Supporting the design and implementation of community-led research projects. Conducting literature reviews and environmental scans. Assisting with data collection, analysis, and
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satellite‑based techniques to help accelerate a sustainable climate transition in the agricultural sector. As a PhD student you will be part of a competent, open, and welcoming research group at Linköping
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this specific structured data. How can we perform inference tasks to learn hidden patterns, like community structure or hidden hierarchies? How can we incorporate domain knowledge to design interpretable models