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Description TUD Dresden University of Technology, as a University of Excellence, is one of the leading and most dynamic research institutions in the country. Founded in 1828, today it is a globally
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life balance we offer flexible working hours, variable part-time, job-sharing models and participation in mobile work (up to 50%). You will benefit from our family-friendly and collegial atmosphere, our
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on a full-time, contractual basis, with sufficient additional funding for fieldwork. The workplace is Halle (Saale), a dynamic, historical town in central Germany (Sachsen-Anhalt) that is home to
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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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calculation of mixture toxicity Development and application of chemical analytical methods Application of physiology-based kinetic (PBK) modelling for the extrapolation of in vitro to in vivo concentrations
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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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features allow overcoming such limitation? The PhD project will be largely experimental with some modelling aspects, and will begin with an identification of a set of research questions based on a detailed