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study design, conduct high-quality omics analyses and statistical and machine-learning based modeling, as well as gaining a deeper understanding in extracellular vesicle biology. Work duties and
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for sensitive personal data is a merit, as well as experience of AI model validation and/or interpretability. About the employment This is a full-time, indefinite-term employment with a six-month probationary
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will address is whether pre-diagnostic metabolic dysfunctions reflected via various levels are associated with pancreatic cancer and could be implemented in future multimodal risk models. The project
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the development, operation, and support of data services with scientific and bioinformatics content, for example AI models and computational applications, FAIR data sharing for infectious diseases, biodiversity
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and further develop its research and education in this area. The department has purpose-built infrastructure for advanced metabolic studies in mouse and rat models. The successful candidate is expected
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of reproducible code by using code sharing platforms, version control, workflow languages and container solutions is a merit. Experience in training deep learning-based models on HPC-resources is also meriting. A
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Development Design new statistical and machine learning models tailored to this emerging omics modality. Multimodal Data Analysis Work with high-dimensional datasets combining quantitative RNA features
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model organisms, their application in conifers remains limited due to challenges associated with tissue structure, nuclei isolation and sensitivity to inhibitory compounds. This project aims to develop