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the group's scientific capabilities. You will have the opportunity to make a significant impact by developing novel workflows that enable the identification, accurate quantification of antigens and associated
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. The postdoc project advertised here will focus on the quantitative studies in Italy and run in parallel with a PhD project focused on the Netherlands. Job description The first phase, consisting of qualitative
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partners at WUR, ICRISAT, the University of Diffa, Sahel Consulting Group, and the Ministry of Environment. Support the project management structure led by PIs Aker and Bulte, including preparation
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that can serve as references for future studies, (ii) enhancing the understanding of physical mechanisms and causal pathways to strengthen attribution analyses and model development, build operationalized
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translate those insights into concrete governance frameworks. In the process we plan to identify a set of independently verifiable safeguards (legal, technical, economic, and governance‐based) that make a
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enabling foundation models to efficiently specialise through the exploitation of structure within representations and across tasks. The resulting methods will help bridge the gap between general-purpose
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competences. You also possess: a PhD degree in food, bio- or analytical chemistry, or in a similar field; experience with carbohydrate related chromatographic and/or mass spectrometric approaches; affinity with
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, workshops, demonstrators, and international collaboration activities within the AutHUMate consortium. Job requirements Essential: PhD in Human-Computer Interaction, Human Factors, Interaction Design or a
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integrated circuits, and more. You will also develop robotic manufacturing techniques to place, align, and integrate these new components into scalable production workflows. You will build and benchmark real
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on the theory of deep learning will investigate questions such as the structure and expressivity of emerging neural architectures relevant to space, such as implicit neural fields, continuous normalising