KCDS Doctoral Projects

An overview of current KCDS doctoral projects sorted by MathSEE method area.

MathSEE Method Area 1

Mathematical structures: shapes, geometry, number theory and algebra

MathSEE Method Area 2

Mathematical modeling, differential equations, numerics, simulation
A Fully Parallelized and Budgeted Multi-level Monte Carlo Framework
Enhancing Reliability of autonomous systems through Integration of Symbolic and Numerical Computation
FAST-DREAM - Faster AtmoSpheric Transport modeling using Dimensionality REduction and Acceleration with Machine Learning
Generation and Scheduling of Activities for Travel Demand Models to Account for Telecommuting Behaviour
HYbrid, NETwork-based modelling of HYdrological systems (HY-NET)
Measure Transportation and Copulas
Modeling and Control of Transport-Dominated Particle Processes
Multilevel Methods for Quality Assessment of Injection Molding under Uncertainty
Optimization and acceleration of aerosol dynamics and chemistry subprocesses in weather and climate models
Optometrological characterization of the vehicle glazing key performance indicators influencing the performance of AI-based algorithms for autonomous driving
Time-dependent tomography for phase space reconstruction

 

MathSEE Method Area 3

Inverse problems, optimization
Generation and Scheduling of Activities for Travel Demand Models to Account for Telecommuting Behaviour
Optimization Enhancements and Uncertainty Considerations in Business Planning
Optometrological characterization of the vehicle glazing key performance indicators influencing the performance of AI-based algorithms for autonomous driving
Theoretical and Empirical Analysis of Matheuristics
Time-dependent tomography for phase space reconstruction

 

MathSEE Method Area 4

Stochastic modeling, statistical data analysis and forecasting
AI-Augmented Discovery of Turbulence-Granular Material Interactions
Artificial Intelligence in bioprocess development
Deep learning methods for probabilistic weather forecasting
Exploring the Potential of Machine Learning for Improving Operational Hydrological Forecasting and Prediction
FAST-DREAM - Faster AtmoSpheric Transport modeling using Dimensionality REduction and Acceleration with Machine Learning
Generative machine learning methods for multivariate ensemble post-processing
Label Efficient Representation Learning for Relation Extraction
Machine learning methods to develop predictive models for estimation of exhaust gas properties from internal combustion engines during cold starts from largescale real-world experimental data
Multilevel Methods for Quality Assessment of Injection Molding under Uncertainty
Multivariate post-processing of sub-seasonal weather regime forecasts
Optimization and acceleration of aerosol dynamics and chemistry subprocesses in weather and climate models
Optimization Enhancements and Uncertainty Considerations in Business Planning
Post-Simulation Diagnostics of Microphysical Process Rates from a Climate and Weather Model with AI
Real-time probabilistic forecasting
Resource efficient probabilistic forecasts in tropical west Africa