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LATICE aims to advance the knowledge base concerning land atmosphere interactions through improved model representation of snow, permafrost, hydrology and large-scale vegetation processes representative of high latitudes, including; 1) New ground observations (gap filling using ML); 2) Land surface model parametrization using data science methods; 3) Seasonal snow cover dynamics using data assimilation and Earth observations (e.g. satellite data, drones).

The CryoGrid community model is a flexible toolbox for simulating the ground thermal regime and the ice/water balance for permafrost and glaciers. The CryoGrid community model can accommodate a wide variety of application scenarios, which is achieved by fully modular structures through object-oriented programming.

Exploring the fundamental constituents of the Universe physicists are faced with very serious calculational bottlenecks. To compare new physics models to data we need to perform very computationally expensive calculations in quantum field theory (QFT).

For maritime safety surveillance we develop new approaches
based on the availability of large arrays of sensors, which
monitor condition and performance of vessels, machinery, or
power systems.

In a wide range of applications, monitoring data streams for faults or changes in behavior (called anomalies) is of great importance.

BigInsight produces innovative solutions for key data-driven challenges facing our consortium of private, public and research partners, by developing original statistical and machine learning methodologies.

The dynamics of Chemical Reaction Networks (CRN), which are typically embedded within Mass and Energy Transport Phenomena such as diffusion or advection, govern the performance of innumerable industrial technologies.

Modern science usually provides both copious amounts of data and complicated models for the part of reality it is trying to describe. Often there is even so much data, and the models so complicated, that it becomes difficult to make full use of the data in deciding which models best describe the world around us, and finding their properties. The main goal of the GAMBIT project is to develop a software tool to help physicists do just that.

Bayesian methods have recently regained a significant amount of attention in the machine community due to the development of scalable approximate Bayesian inference techniques. There are several advantages of using Bayesian approach: Parameter and prediction uncertainty become easily available, facilitating rigid statistical analysis. Furthermore, prior knowledge can be incorporated.