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Field
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environment, of engineering AI solutions to problems (especially neural networks or large language models) and/or applying data science techniques (such as Bayesian or similar statistical modelling). You should
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learning, path planning, and Bayesian optimization. Plan and perform experiments, oversee research performed by students and research technicians, and mentor and train postdoctoral fellows, graduate students
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techniques (e.g., techniques for differentiating effectful programs such as gradient estimation of probabilistic programs, implicit function differentiation, compositional Bayesian inference techniques
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model fitting, including Bayesian model fitting, is desirable but not essential. Familiarity or experience of management and analysis of large multidimensional real world data sets using Stata, R, Python
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computing (HPC) environments and include data assimilation techniques in a Bayesian framework. Under the guidance of a mentor, the participant will identify and integrate multiple data streams into the model
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-level models, Bayesian inference, latent class analysis) Strong data visualization skills using packages such as ggplot2, seaborn, or matplotlib Experience with clinical research databases and data
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exploration strategies that go beyond traditional techniques such as linear programming or deterministic solvers. You will work on cutting-edge methods including: Bayesian optimization Surrogate modeling
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, and social sciences scholarship across the school. Examples of topic areas include (but are NOT limited to): models for inference (e.g., SEM/CFA, Bayesian modeling, linear mixed effects), data mining
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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