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for basic research. The successful applicant will contribute to the further development of the Coincidence Analysis (CNA) method for causal data analysis. Currently, CNA has four key limitations: (I
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neurotechnologies. The goal is to contribute broadly to research on applications of AI in medicine, and in particular to the development and validation of novel computational language models, algorithms, and tools
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project bridges two foundational fields in computer science and mathematics: Theory of Algorithms and Extremal Combinatorics. By integrating these areas, the project seeks to develop innovative
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of patients. The candidate will contribute broadly to the project to enable the development of new algorithms for clinical AI based on patient data from heterogeneous sources, notably language/speech-based
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and application of fast solvers for Maxwell’s equations and nonlinear inversion algorithms that we have already developed in a previous PhD project. In addition to electromagnetic geophysics
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electromagnetic data during drilling. This includes the further development and application of fast solvers for Maxwell’s equations and nonlinear inversion algorithms that we have already developed in a previous
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with radioactive sources and at beamlines to test the performance of the NOVO prototype. Contribute to the development and testing of AI-algorithms for image reconstruction. Support NOVO staff working
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interactions need to be strengthened, integrating the extracted requirements into services, prototyping the necessary interfaces/software, and iteratively testing via case studies. The development
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information of the data to make a prediction using advanced mathematical tools. This insight opens the door for enjoying the real world. The candidate further develops efficient and robust algorithms
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technological progress in our increasingly digital, data-driven world. Researchers in Integreat develop theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data. By