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role in enabling this transition, while also introducing new challenges related to system dynamics, stability, and control. This project focuses on the development of modeling, stability analysis
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, capable of growing on marginal land and contributing to carbon emission reduction. However, the main commercial variety is a naturally sterile triploid hybrid limiting opportunities for improvement through
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one year of experience in applying analysis and synthesis methods and research methodologies in computer and communications engineering or computer science. The successful candidates will contribute
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Technology Institute Groningen (ENTEG). The project aims to address timely challenges in the analysis, design, and operation of complex socio-technical systems, such as energy and transportation networks. What
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. This is precisely where this doctoral thesis comes in. At the Institute of Climate and Energy Systems – Jülich System Analysis (ICE-2), we are researching the systemic prerequisites for a successful energy
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Student (m/f/x) for a MSCA-DN project starting October 1,2026. The position is initially limited to 24 months max. until November 30, 2028. An extension to allow completion of the PhD project is
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of materials collected in fieldwork, for acknowledgment of sources and collaborators. Provide necessary social and cultural contextualization of transcriptions/translation, and contribute to comparative analysis
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 2 months ago
, spatial references, or prosody. As a result, existing systems often depend on limited gloss-annotated data, generalize poorly across signers and recording conditions, and produce translations that lack
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independently; conduct translational studies in the lab; lead scientific article preparation, data analysis, and writing; attend regular team and project meetings; apply for grants; and develop a portfolio of
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-world environments inevitably face noisy data, distribution shifts, and situations their training never anticipated. Existing machine learning research focuses on limiting the impact of such disturbances