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DSTrain
Research Areas 2025 (Call 2)
Mathematics & Statistics
Bayesian machine learning
Causality-Inspired Machine Learning
Double descent and sufficiency
Explainable and scalable statistical models for complex data
Fluid Mechanics
Gradient boosting for time-to-event analysis
Risk, reliability and machine learning
Risk evaluation, control and machine learning
Risk management in renewable energy markets
Stochastic filtering, numerics, and machine learning
Stochastic modelling of natural processes
DSTrain
>
Research Areas 2025 (Call 2)
>
Mathematics & Statistics
Mathematics & Statistics
Research Themes
Bayesian machine learning
Causality-Inspired Machine Learning
Double descent and sufficiency
Explainable and scalable statistical models for complex data
Fluid mechanics
Gradient boosting for time-to-event analysis
Risk, reliability and machine learning
Risk evaluation, control and machine learning
Risk management in renewable energy markets
Stochastic filtering, numerics, and machine learning
Stochastic modelling of natural processes