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Background information about the project: Our team recently won the Metascience Novelty Indicator Challenge . We have been developing a method that uses Large Language Models (LLMs) to analyze
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(www.denkglobaldx.org ), our department hosts a diverse and interdisciplinary team working across diagnostic innovation, clinical and implementation research, mathematical modelling, data science, and health-economic
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at international conferences Your profile Completed Master's degree (or equivalent) in Computer Science, Data Science, Computational Linguistics, Mathematics, or a related field Strong programming skills in Python
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clinical data, high-end GPU resources, and integration into TWIN-X, an EU Horizon Europe consortium with 18 partners from 12 European countries. You will work with data from TUM University Hospital and
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helium cryo-magnetizing stage. Your tasks in detail: 1. Experimental design and data acquisition Performing structural and chemical characterization of specimens using advanced TEM techniques, including
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As part of the German government's artificial intelligence (AI) strategy, the successful Saxon competence center ScaDS.AI Dresden/Leipzig (Center for Scalable Data Analytics and Artificial
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on futuristic technologies in the field of machine learning and computer vision. Hence, we investigate and develop an innovative computation-in-memory (CIM) solution for artificial intelligence accelerator design
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the recruitment of participants, MRI and behavioral data acquisition, data curation, data analysis and publication of the results. There are no formal teaching requirements. Additional responsibilities include
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 2 months ago
25 Jul 2026 Job Information Organisation/Company Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt Research Field Medical sciences Researcher Profile First Stage
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of heterogeneous operational data. However, limited availability, privacy constraints, and the variable quality of real-world datasets continue to hinder the development of reliable AI solutions for energy system