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processes as well as the impact of biological processes on materials. Our ambition is to foster a dynamic teaching and research environment that is internationally recognized for its excellence in connecting
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is part of the national research programme DDLS. Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels
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cell-cell interactions to disrupt cancer-promoting equilibria. We aim to develop in silico and in vitro models and tools to build and validate digital twins of such interactions, with the objective
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experience in areas such as advanced image analysis, data structuring and data integration, and large language models (LLMs) with applications in biological and biomedical research. The successful candidate´s
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workflows. •Operation and monitoring of LC-MS/MS and LC-HRMS instrumentation. •Routine quality control and assessment of analytical performance. •Documentation of laboratory work and analytical results
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in advanced national postgraduate courses and training events. Qualification requirements The candidate should have a Ph.D. or similar working experience in Image analysis, Bioinformatics
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multi-hour process that remains largely uncharacterized in biophysical terms. Using in-vivo-mimetic systems, this project will investigate how these parasites find, penetrate, migrate through, and exit
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on studying genetic variation and determining how different variants co-occur on the same paternal or maternal haplotype, a process known as haplotype phasing. We have previously developed methods for haplotype
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involves data sources from large population-based cohort, national registry-based cohort, and clinical imaging cohort. The student will learn how to conduct high-quality epidemiological studies including
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qualifications Publications at top machine learning or computer vision conferences (NeurIPS, ICML, ICLR, CVPR, AISTATS etc.) are highly meriting. Expertise in Bayesian methods, generative models, multimodal models