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Brandenburgische Technische Universität Cottbus | Cottbus, Brandenburg | Germany | about 2 months ago
candidate is expected to incorporate innovative approaches into their research that connect classical probabilistic models with modern deep learning architectures. Examples include Bayesian deep learning
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Implementing Bayesian networks and uncertainty quantification techniques to account for sensor noise and model confidence limits Designing, training, and fine-tuning computer vision models to extract clinically
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., R, Python, Julia), preferably including scientific software development. Knowledge of ecological statistics (frequentist and/or Bayesian), particularly spatial statistics and nonlinear models
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, including active learning or Bayesian optimization. Experience with imaging, time-series or high-dimensional data. Exposure to crystallography or structural biology. Experience with multimodal datasets and
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Bayesian methods, are encouraged to apply. Minimum Qualifications PhD in Statistics or closely related fields with documented research in statistics For the Assistant Professor position, candidates must
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to have experience in several of the following areas: data processing, statistical analyses, R software, regression models, process-based models such as DSSAT or APSIM, Bayesian statistical analysis
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editorial oversight. Familiarity with the concepts of Bayesian benchmark dose (BMD) modeling Special Instructions to Applicants: For full consideration, applications for job number 540019 should be both
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, mixed-effects modeling, Bayesian methods, deep learning, variational autoencoders, generative AI). Is an experienced programmer in R and/or Python, and used to working with large datasets and reproducible
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modelling, ecological forecasting, time-series analysis, Bayesian statistics, and statistical programming (primarily in R and Stan). You will gain experience working with large, long-term, multidimensional
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(PINNs) and surrogate modelling Time-series modelling and anomaly detection Bayesian methods and uncertainty quantification Graph Neural Networks (GNNs) Spatiotemporal data engineering Digital twins and