Our first poster event took place on-site at the Super C, RWTH Aachen. The event was created to enable knowledge exchange and to build cooperation between the doctoral researchers.
Two talks were given by doctoral researchers:
- Timo Stomberg - Exploring Wilderness Using Explainable Machine Learning in Satellite Imagery
- Daniel Wolff - Physics-Informed Neural Networks as Reduced Simulation Models for Engineering Applications
Several posters were presented by doctoral researchers:
- Alaukik Saxena - A materials informatics framework to discover patterns in atom probe tomography data
- Alessio Quercia - Sample importance to learn a given task
- Anna Lara Simson - Real-time ice characterization with autonomous melting robot
- Ann-Kathrin Edrich - A modular and scalable workflow for data-driven modelling of shallow landslide susceptibility
- Aytekin Demirci - Data Mining and Machine Learning of Dislocation Systems
- Bamidele Oloruntoba - High Resolution Land Surface Modelling over Africa: the role of spatial and temporal resolution
- Cristiano Köhler - Tracking the provenance of electrophysiology data analysis results
- Danimir Doncevic - Optimal Reduced-Order Models for Energy System Optimization
- Dwaipayan Chatterjee - Understanding cloud systems structure and organization using a machine’s self-learning capability
- Eike Cramer - Conditional Normalizing Flows for Data-Driven Scenario Generation
- Emile de Bruyn - Finding Features in 4D Distributions of Ions to Speed Up Drug Discovery
- Felix Terhag - Parameter-Free Uncertainty Estimates for Volume Predictions in Heart MRIs
- Giuliano Santarpia - RepOdor: A Manually-Curated, Comprehensive Repository of Odorants and their OlfactoryReceptors
- Ingo Steldermann - Moment Approximations for Shallow Flow
- Jan Rittig - Computer-Aided Molecular Design with Graph Machine Learning
- Jazib Hassan - Hybrid Process Modelling Combining Mechanistic Equations with Machine Learning
- Johann Fredrik Jadebeck - Madness of Crowds or Sanity of Herds? Rational Uncertainty Quantification for 13C Metabolic Flux Analysis
- Johannes Kruse - Explainable AI for Power Grid Stability and Control
- Johannes Seiffarth - Automated Quantitative Analysis for Microbial Live-Cell Imaging
- Johannes Wasmer - Boosting Simulations of Quantum Materials with Machine Learning
- Jorge Guzmán - Hybrid models for cellular signalling: meso-scale pathway identification
- Karel van der Weg - Improved classification of protein function by a localized 3D protein descriptor and deep learning
- Karina Ruzaeva - A hybrid multi-object segmentation framework with model-based B-splines for microbial single cell analysis
- Laura Helleckes - Bayesian Optimisation Meets Robotic Workflows: Data-Efficient Characterisation of Bacterial Strains Using Thompson Sampling
- Leonardo Boledi - Computational multi-physics modeling to predict the performance of melting probes in ice
- Lisa Beumer - Verification from Space – Building Transparency and Confidence through Earth Observation Big Data
- Marcel Zimmer -Entangled Gaussian Processes for Digital Twins of Power Systems
- Mario Rüttgers - Machine learning in flow simulations
- Maximilian Siska - Towards Automatic Experimentation and Discovery in Bioprocess Development
- Sonja Germscheid - Stochastic scheduling optimization of industrial processes under electricity price uncertainty
- Sophia Wiechert - Efficient Importance Sampling via Stochastic Optimal Control for Stochastic Reaction Networks
- Stephan Malzacher - BioCatHub: Research data management based on the FAIR data principles in Biocatalysis
- Viktor Grimm - Prediction of flow with Neural Networks - A Physics-Aware Approach for 2D-Flow
