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Researcher in Numerical Modeling of Sea Ice We are seeking a highly motivated Doctoral Researcher to join our ice mechanics research group at Aalto University to work on collaborative project between Aalto
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the contexts of sustainability transitions governance and urban transformations. ( https://nodus.aalto.fi ) We are now looking for a Postdoctoral researcher in complexity-aligned innovation and institutional
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-driven atomistic modeling and AI-accelerated cheminformatics to join the Otaniemi Center for Atomic-scale Materials Modeling (OCAMM), hosted by the Department of Chemistry and Materials Science (CMAT
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detection are necessary. This project develops watermarking methods and software for speech and audio generative models, focusing on robustness and suitability for open-source use. Your role and goals As a
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generalization. Your work will include: Developing methods for detecting the authenticity and origin of speech and audio content generated by multiple models and containing different watermarking methods. Creating
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the contract starts; you can apply for these positions already before you have your degree. We value expertise in domains such as graph algorithms, parallel algorithms, computational complexity theory, models
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refine LCA models to assess the environmental impacts of bio-based products and systems using software tools such as OpenLCA. Analyze complex datasets to identify key areas for improvement in environmental
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innovating towards sustainability, circular economy, governance and institutions for sustainability, sustainability transitions and business models for sustainability. This work offers a basis for rethinking
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(fulltime). We value work-life balance and well-being in all aspects of life. We work in a hybrid model, with the primary workplace located at the Otaniemi Campus in Espoo, Finland. Life on the revitalized
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‑classical algorithms to tackle domain‑specific, computationally demanding problems. Methods will be designed for near‑term (NISQ‑era) deployment, with models evaluated for expressivity, training stability