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iMIRACLI

Innovative Training Network

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About
People
Projects
Training
Admissions

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Research Projects

Detection of aerosol-cloud interactions in observations space

(Filled)

Detection of aerosol-cirrus cloud interactions in satellite data: process-oriented approach

(Filled)

Algorithms for multimodal data-based inference and prediction for climate science applications

- Open -

Causal discovery in the presence of multiple time scales

- Open -

Causal discovery in the presence of latent variables

- Open -

Physics-aware and explainable machine learning for dust and cloud properties retrieval

(Filled)

Scalable and Expressive Spatio-Temporal Modelling

(Filled)

Academic Hosts

University of Oxford

Universität Leipzig

Stockholm University

Eidgenössische Technische Hochschule Zürich

University of Edinburgh

Universitat de Valencia

University College London

German Aerospace Center (DLR)

Ecole Polytechnique Federale de Lausanne (EPFL)

Partners

Iris.ai

FastOpt

Met Office

The Alan Turing Institute

Amazon

GAF AG

University of Jena

Funding

This project receives funding from the European Union’s Horizon 2020 research and innovation programme under Marie Skłodowska-Curie grant agreement No 860100.

Contact

iMIRACLI Project Office

Prof. Philip Stier

Department of Physics

University of Oxford

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Detection of aerosol-cloud interactions in observations space

Detection of aerosol-cirrus cloud interactions in satellite data: process-oriented approach

Clouds as sources and sinks of aerosol – micro- vs. macrophysical processes

Causal inference in satellite data studies of aerosol-cloud interactions

Isolating aerosol effects in multiple heterogeneous satellite datasets

Complexity reduction to understand global aerosol-cloud interactions

Identification of cloud regimes relevant for aerosol-cloud interactions

Detecting and attributing aerosol influences on large-scale climate

Understanding Arctic clouds:how do aerosols control their properties

Selection bias in observational studies of aerosol-cloud interactions

Algorithms for multimodal data-based inference and prediction for climate science applications

Causal discovery in the presence of multiple time scales

Causal discovery in the presence of latent variables

Physics-aware and explainable machine learning for dust and cloud properties retrieval

Scalable and Expressive Spatio-Temporal Modelling

Academic Hosts

Partners

Funding

Contact