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project brings together PhD students and senior researchers from multiple disciplines to tackle challenges in sustainable aluminium. The aim of RAM3 project is to enable the broad application of recycled
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these institutional arrangements influence public space outcomes such as accessibility, safety, social value, and sustainability. Key questions include: How can governance structures better coordinate the multiple
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of scientific works). If it is difficult to judge the applicant’s contribution for publications with multiple authors, a short description of the applicant’s contribution must be included. About The Faculty
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for tabular-native models. This can involve, for example, studying new TRL model architectures, serialization and tokenization techniques, among others. A strong interest and background in AI and/or NLP
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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Charger). In this role, you will design scalable power converters and intelligent BMS architectures for mobile charging platforms, contributing to the prototyping and real-life testing of a modular mobile
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Leibniz Institute of Ecological Urban and Regional Development (IOER) • | Dresden, Sachsen | Germany | about 4 hours ago
investigate dynamic interactions between ecosystems and society at and across multiple spatial scales as well as options for responsible stewardship. Our research is highly interdisciplinary across the natural
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findings at meetings and/ or conferences. Lead the definition and documentation of requirements, architecture and design of secure, scalable, asynchronous, agentic systems, based on knowledge of principles
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), and the Faculty of Science of the University of Amsterdam. About Research group The Database Architectures (DA) research group of CWI is well known as a leading data systems research group, active in
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of structures, facilitating a form-finding process driven by FEM analysis. Training deep learning algorithms to suggest multiple structural concepts tailored to specific boundary conditions. Expanding FEM