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to the analysis of time series. In particular, the project will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show
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-value reinforcements in their short and randomly aligned form. A key challenge to the effective reintegration of recycled carbon and glass fibres into high-performance products lies in achieving scalable
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synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application to the analysis of time series. In particular, the project
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Background and Research Environment Recently established, the Cancer RNAs & Epigenetic Group (CREG) focuses on understanding the role of epigenetic and epitranscriptomic regulation in tumor
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Max Planck Institute for Plant Breeding Research, Köln | Koln, Nordrhein Westfalen | Germany | 5 days ago
Job Offer from July 10, 2025 Research Assistant (m/f/d) at Single Cell Genomics Platform We are looking for a single cell and genomics specialist with a proven track record in the use and
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data integration, cancer systems biology, and single-cell analysis, with strong links to clinical and translational oncology. Our work is based in the dynamic research environment of Heidelberg
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analysis is complemented and correlated with circulating tumor DNA (ctDNA), as well as immune cell composition in serial blood samples collected during therapy. The work is carried out in close collaboration
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Failure Analysis of Composite Sleeves for Surface Permanent Magnet Electrical Machines This exciting opportunity is based within the Power Electronics, Machines and Control (PEMC) and Composites
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Ref.: 532662 Work type: Full-time Department: School of Chinese Medicine (22100) Categories: Senior Research Staff & Post-doctoral Fellow Applications are invited for appointment as Research
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performance and university-specific criteria. Integrating multimodal analysis (voice tone, facial expressions, sentiment analysis) for holistic assessment. Ensuring fairness and bias mitigation in AI-driven