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processes related to carbon cycling in the soil-plant system Experience with Bayesian inference and machine learning is an asset Ability to work independently and cooperatively as part of an interdisciplinary
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analytical skills for model formulation and optimization Demonstrated research potential, ideally with a track record of publications in relevant venues (journals such as IEEE T-ITS, INFORMS Transportation
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and mortality registry, community-embedded settings for participatory research, and cutting-edge methodological expertise in causal inference and artificial intelligence methods for epidemiology and
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transcriptomics and single-cell RNA sequencing on patient samples • Mining and analyzing public cancer databases (TCGA, GEO, etc.) and omics data • Inferring TLS formation and maturation stages from
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challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers
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, R) Expertise in machine learning, Bayesian statistics is beneficial Capacity for interdisciplinary teamwork and excellent communication skills Ability to communicate in English fluently
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Retrieval-Augmented Generation (RAG) for data retrieval and knowledge inference implementation of your machine learning pipeline in Python (using e.g. PyTorch) validation of your results in collaboration with
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, to characterize immune cell dynamics in murine models of inflammation and cancer. RESPONSIBILITIES: Developing and performing computer simulation of MRI contrast of labelled cells and tissue Labeling and tracking
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enable the model to infer health-related information directly from NMR spectra of human blood. To this end, the model will be pre-trained using self-supervised learning on large-scale, partly synthetic
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. Bonus lectures can be picked by the students depending on their interests and project-specific requirements. Students can deepen their knowledge about selected topics (e.g. Bayesian Statistics, HMMs, AI