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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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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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inference implementation of your machine learning pipeline in Python (using e.g. PyTorch) validation of your results in collaboration with colleagues from various application areas publication and
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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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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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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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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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populations from New Guinea using museomic data Assembling high-quality reference genomes and generating whole-genome resequencing data of avian skins Inference of evolutionary history using Ancestral
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(e.g. Bayesian Statistics, HMMs, AI, advanced programming in Python) in small classes of max. 10 participants. Lecture series: QMB students suggest, invite, and host external speakers at this event