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, computer science, and statistics The objective of this PhD project is to develop machine learning algorithms that perform efficiently and coherently across both classical and quantum computing platforms. The PhD
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Sapere Aude – dare to know – is our motto. Our students and employees develop important knowledge that enrich both the individual and the community. Our academic environment is characterised by
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algorithms for microscopy image analysis problems (primarily 2D timelapse data), which are driven by real applications in life science research Developing solutions to integrate large foundation models
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embedding longevity, material efficiency, and realistic performance limits from the start. This project develops a pioneering methodology for data-driven optimization of next-generation material systems. You
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nuclear and particle physics research leveraging machine learning and AI for data analysis and detector development, as well as exploratory work in quantum algorithms, depending on background and interests
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heterogeneous multi-omics datasets. Integrative Data Analysis: Perform and lead analysis of large-scale multi-omics datasets, including RNA/DNA sequencing, methylation, and metabolomics. Method Development
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Design, develop, and implement advanced algorithms, models, and software tools for spatial data analysis, machine learning, and AI-driven geospatial applications Lead and collaborate on interdisciplinary
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of malignancies, blood disorders, and experimental therapies. Job Summary The Data Science, Analyst will will work to maintain and deploy algorithms for accurate detection, segmentation, and classification of cells
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intelligent transportation systems (ITS), with an emphasis on developing and deploying state-of-the-art Large-Language Models (LLMs), Vision-Language Models (VLMs), and Vision-Language-Action (VLA) models
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, particularly Python; (ii) use of parameter optimization algorithms, particularly PEST and PEST++; (iii) remote sensing applied to the water cycle; and (iv) application of machine learning techniques to spatio