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, as part of the development, one position located in Görlitz as Research Associate / PhD student (f/m/x) Integration of CMOS detector technology into the Universal Bayesian Imaging Kit (UBIK) (subject
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and Bayesian methods) and population studies (nonlinear mixed-effects modeling methods) and hypothesis testing Comprehensive knowledge of the concepts underlying physiological modeling and systems
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and Bayesian methods. Knowledge of statistical software, particularly R. Strong statistical programming skills. Understanding of clinical trials. An ability to work well both on own initiative and
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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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. Experience in the implementation of mathematical or statistical models and model fitting, including Bayesian model fitting, is desirable but not essential. Familiarity or experience of management and analysis
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- based neural networks, Bayesian statistics, and text analytics are a must. Nice to Have: Experience developing and integrating APIs for healthcare systems to ensure seamless interaction with AI models
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expertise in machine learning and/or Bayesian models is preferred. This position will involve both methodology development and analysis of multi-omic sequencing data, including spatial transcriptomic data
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development. Experience with implementing statistical learning or machine learning (e.g. Bayesian inference, deep-learning). Programming skills in Python and experience with frameworks like PyTorch, Keras, Pyro
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selection criterion in some extent. This strongly suggests revisiting the study of these latent variable models with a Bayesian point of view and to understand how this evidence lower bound integrate implicit
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possible thereafter. The aim of this project is to advance the development of multi-trait Bayesian linear regression models that enable the sharing of genomic information across traits and biological layers