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Field
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, with the following qualifications: a Master's degree in Computational Biology, Applied Mathematics, Physics, Computer Science, or a related field; enthusiasm for learning the biological background and
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framework to enable meta-analysis of multiple large biobank datasets, all of this with the aim of increasing our sensitivity to unravel the complex genetic causes of disease and, in so doing, identify new
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are persistent challenges. Additionally, each local context brings its own social, ecological, and institutional complexities, making it difficult to scale up successful examples. In this context, we seek
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integration, especially in molecular medicine; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time
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spanning a broad range of research areas - including methods for high-dimensional data and data integration, especially in molecular medicine; mathematical modelling of cancer; probabilistic modelling and
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its rich information content, conventional analysis methods have not yet fully realized its potential. This research project aims to develop a robust AI foundation model based on modern Transformer
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of Civil and Mechanical Engineering, Thermal Energy Section. We look for a talented, self-motivated, and team-oriented individual who thrives in a collaborative environment and enjoys tackling complex topics
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equipment like 3D printers, furnaces, centrifuges, and microfluidic devices. Meticulous data recording and analysis are essential, as the project combines practical engineering with fundamental physical
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from single bioinoculants to complex synthetic microbial communities (SynComs). Join us as PhD candidate and contribute to a multidisciplinary effort at the intersection of microbiome ecology, genomics
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to scale up and demonstrate sustainable processes for industrial (bio)manufacturing of pharmaceuticals by integrating environmentally friendly technologies and processes. However, given the complexity