Peatlands regulate atmospheric greenhouse gas concentrations and thus the global climate. They form one of the largest terrestrial C stores and current and projected long-term shifts in temperature, ...

This study investigates the potential of probabilistic classification to enhance credit-scoring accuracy, with a focus on model validation through reliability thresholds. By quantifying prediction ...

A typical dilemma is a choice between two options. However, today’s innovators and CIOs face a different challenge of dealing with both probabilistic and deterministic code, not separately, but ...

Accurately tracking atmospheric greenhouse gases requires not only fast predictions but also reliable estimates of uncertainty. Researchers have developed a lightweight machine learning framework that ...

In this project, the student will work on probabilistic modelling and machine-learning techniques to advance the current description of nuclear reactions in specific energy regimes of astrophysical ...

Samuel Kaski’s research group Probabilistic Machine Learning is searching for postdocs to work on AI fundamentals in exciting projects. The work includes collaboration with ELLIS Institute Finland, ...

Overly optimistic availability assumptions are contributing to a clear discrepancy between modelled PV output forecasts and reality. Image: SeaBrook Solar. PV system modelling relies on a standard ...

Historical loss models and fire maps insurers use are in need of a tune-up, and experts have turned to artificial intelligence to do the job. Traditional wildfire models rely on static historical data ...