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» Modelling tools Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 15 Sep 2026 - 00:00 (Pacific/Saipan) Country Germany Type of Contract Temporary Job Status Full-time
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on the solid application of scientific research method. The department has a pluralistic approach to research methods that includes, for example, formal theoretical models, quantitative empirical methods such as
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production. By developing hybrid architectures combining ontologies, generative models, reinforcement learning, and uncertainty quantification, the PhD project addresses the challenges identified by ICCARE in
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
seeks to strengthen the bases for the design, use, and deployment of digital twins, addressing key open challenges in model hybridization, composability, development methodologies, digital coupling, and
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at the Laplace laboratory, where numerical and analytical models are being developed to predict plasma potential control and flux entrainment from polarized electrodes. During the third year of the thesis project
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11 Jul 2026 Job Information Organisation/Company University of Amsterdam (UvA) Research Field Biological sciences » Biological engineering Biological sciences » Biology Computer science » Modelling
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) offers 1 fully funded PhD position “Architecture Analysis and Models Development of DC Micro Grid for New Gateway Electrical Power Subsystems” within this international research network, providing
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-based AI for multidisciplinary tumor boards. The successful candidate will develop LLM-based pipelines, semantic harmonization, and transformer-based temporal models on a large multimodal oncology dataset
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. The research will involve training machine-learning models on large structure and sequence datasets and integrating membrane-specific biophysical constraints to enable the design of membrane proteins and
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-friendly software tool, based on state-of-the-art machine learning models [1], that can predict the outcome of HFNC therapy in individual patients with higher accuracy than current clinical indices, early in