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Generation in Context: Object- and Interaction-Aware Generative AI Models of Language Use”. Preferred start date is 1 February 2026 but is negotiable. Job descriptionFace-to-face conversation is the primary
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design. Objectives: The main objective is to support the development and validation of morphing drone prototypes by combining structural optimization, lightweight design, and experimental testing
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Research Framework Programme? Other EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Objective 1: The goal is to develop a medical device incorporating
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personal sphere of play: We are excited to announce a Postdoctoral Researcher position in the group of Prof. Gunda Köllensperger. The major objective is to downscale metabolomic techniques to the level of
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MOFs and their hybrid materials. The primary objective is to explore the fundamental structure–property relationships that govern MOF vitrification and liquid-phase behavior, and to develop new MOF-based
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 1 month ago
the objective of simulating the complete ECT problem, 2 main challenges have to be faced, and will be investigated during the post-doc. First, the 3D meshing challenge. The difficulties include embedded
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: We are excited to announce a Postdoctoral Researcher position in the group of Prof. Gunda Köllensperger. The major objective is to downscale metabolomic techniques to the level of organelles, small
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transmission cables; Hydrodynamic and VIV modelling; Experimental and numerical VIV studies; Developing research objectives, projects and proposals; Conducting individual or collaborative research projects
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completed, or be close to completing, a PhD/DPhil in a relevant quantitative field together with a demonstrable track record in studying humans and machine learning models. Advanced programming and
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(MERCE). The main objective is to develop safe planning and reinforcement learning algorithms with various degrees of confidence for variants of Markov decision processes. More precisely, we will develop