15 bayesian-inference-"Integreat--Norwegian-Centre-for-Knowledge-driven-Machine-Learning" PhD positions in Germany
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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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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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analyses, an area in which our group has a track record of success (see recent publications below). The TARGET-AI project seeks to apply leading-edge techniques from deep learning and Bayesian modeling
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, using techniques such as: High-dimensional data mining Tensor decomposition Causal inference Statistical process modeling Machine Learning Applications include public transport, private vehicles, traffic
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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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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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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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to reason about software (e.g., LLM agents for finding and fixing bugs) Static and dynamic program analysis (e.g., to infer specifications) Test input generation (e.g., to compare the behavior of old and new
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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