The successful candidates will be based in the Universitat de València, Spain. Prof. Camps-Valls is the coordinator of the Image and Signal Processing (ISP) group. The group is devoted to the development of machine learning and signal processing techniques for remote sensing image processing, Earth observation data analysis and the Geosciences. Several topics are treated in our research group and projects: regression, causality & information theory, Earth observation data analysis, physics-aware machine learning, generative modeling, eXplainable AI and feature ranking, and anomaly detection. Applications in Earth and Climate sciences fields.
We are always looking for PhD students and postdocs to work in the intersection of Machine Learning and Earth/Climate Sciences. We are searching for outstanding, highly motivated students with a Master/PhD in computer science, statistics, machine learning, electrical engineering, physics, or mathematics. Good programming skills, a critical and organized sense for data analysis, as well as maturity and commitment, strong communication, presentation and writing skills are a big plus.
Positions are open in the context of the following research projects:
Blink, a new deep network is applied in Earth sciences. But, what has the networked learned? Why? and for maximizing what? This project aims to develop new data analysis techniques in mathematics and computer sciences to explore the solution space of deep architectures, with special focus on Earth observation problems. We will explore invariances in latent spaces, deep causal methods, and basis visualization. Experience in deep learning, signal and image processing, color vision, wavelets and proficiency in maths are required.
H2020 XAIDA -- Extreme AI for Detection and Attribution [2 postdocs, 1 PhD]
The Earth is a complex dynamic network system and in the last few hundred years human activities have precipitated enormous changes in the Planet. It goes without saying that the most important challenge for today's Science is to detect and attribute the causes of such changes. We will develop, characterize, and apply novel anomaly detectors, under the framework of Gaussianization networks, Variational Autoencoders and Normalizing flows, for spatio-temporal detection of extreme events. Applications on droughts and heatwaves will be tackled. A second position will focus on associating impacts on biosphere and society with advanced regression models, Bayesian deep learning and deep GP regression to perform uncertainty quantification, automatic variable relevance determination, and error propagation. Detection of rare, unexpected changes and events under the developed statistical framework will constitute the stepping stone before the more ambitious far-end goal of machine attribution of anthropogenic climate change causes. Experience in deep neural networks, Earth and Climate sciences are required.
Machine learning models are widely used to learn both the forward and inverse functions, and now routinely replace complex models and sub-components to improve scalability and mathematical tractability. These models are commonly known as emulators and report excellent accuracy-speedup trade-offs compared to simulators, besides elegant ways to do uncertainty quantification, error propagation, and sensitivity analysis. In this project we will focus on probabilistic machine learning models, and in particular on Gaussian processes (GPs), which have excelled in both prediction and emulation. As any other data-driven technique, however, they do not necessarily respect physical or causal relations. Furthermore, GP models are still computationally costly and often yield predictions that are inconsistent with physics principles. Combining model simulations and observational data is an interesting way, and has been recently approached with joint GPs and multifidelity GPs. Nevertheless, advances are still needed in terms of efficiency, physical plausibility, and interpretability. Experience in Gaussian processes, Bayesian inference, and remote sensing are required.
We are looking for outstanding postdoc candidates with a strong interest in machine learning and geosciences to cover several PhD and post-doc positions. The topics of the project are: (1) Machine learning for new observational products improving spatial and temporal resolution by downscaling using recurrent and convolutional neural nets; (2) Hybrid machine learning-physical modelling that preserve mass and energy but also blend observations and models synergistically to improve extrapolation and uncertainty quantification and propagation; and (3) Machine learning for detecting and understanding modes of variability, extreme events and memory effects in observations and models using dimensionality reduction, multi-variate anomaly detection, and causal discovery.