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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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Bayesian belief networks; Experience in scenario development approaches, e.g. SSPs; Experience in the application of R-based analytical tools for qualitative or semi-quantitative modelling, incl. RQDA
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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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control, state estimation, and path planning algorithms for single and multi-agent robotic systems (UAVs). develop and train AI models for practical applications such as real-time object detection and
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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
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of individual E. coli strains will be investigated. The objective is to characterize metabolic interactions between E. coli isolates in the gastrointestinal tract, utilizing, among other methods, larvae
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-driven process control. A central objective is the development and optimization of robust, low-maintenance, and cost-effective sensor systems capable of continuously monitoring COD and other key parameters
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of permanently removing CO2 from the air. The DACStorE project investigates three technologies: low-temperature, high-temperature, and electro-swing DAC. The specific research objectives are to: Conduct a
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be inferred from models that are incomplete and data that involve errors. For such challenges, Bayesian analysis using Markov Chain Monte Carlo (MCMC) has become the gold standard. For addressing high