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The annual internal retreat is designed as a platform where doctoral researchers inform each other about their latest
research results and open issues. It is a forum for knowledge transfer and information exchange. It follows a Docs‐for‐ Docs concept, where they select content and conference style. The doctoral researchers give presentations on current research topics (both results and open issues). The coordinator team organizes the retreat, which contain social program incl. institute visits.
December 6, 2021
"Gaussian processes for sensitivity analysis, uncertainty quantification, and Bayesian inference in landslide research"
"Uncertainty Quantification of Hybrid Models in Life Sciences"
"An approach to tracking the provenance of electrophysiology data analysis"
June 10, 2021
“Optimal neural network models for energy system optimization”
“CODOR’: A manually-curated COmprehensive Database of Olfactory Receptors and their ligand”
“Optimizing Spiking Neural Networks with Learning to Learn”
“Geometry-aware image analysis for microfluidic live-cell experimentation”
June 11, 2022
“Ion-Residue Interaction of an Intrinsically Disordered Protein Surface: Gathering Inputs for Deep Learning”
"Land surface modelling over Africa: The role of spatial and temporal resolution"
“Hybrid process modelling combining mechanistic equations with machine learning”
“Using neural networks to explore local chemical function in proteins”
June 4, 2020
“Data-based prediction of power system operation and stability”
“Time-series analysis: Kramers-Moyal and MFDFA in stochastic time series”
June 9, 2020
“Machine Learning and Bayesian Methods in Neuroscience”
"BioCatHub: A platform for standardised data acquisition in biocatalysis according to the FAIR data principles"
June 22, 2020
“Latent Space Distribution Learning in Energy Systems using Normalizing Flows”
“Bayesian Modelling for Uncertainty Quantification in Metabolic Network Inference”
June 26, 2020
“Predicting the flow in patient-specific aneurysm geometries using convolutional neural networks”
“Uncertainty Estimation in Deep Neural Networks”