Ph.D. position - Machine Learning for Geosciences

We are searching for an outstanding candidate with a strong interest in machine learning and geosciences to cover one PhD student position to join the Image and Signal Processing (ISP) group in the Universitat de Valencia, Spain, The position is fully funded by an ERC Consolidator Grant 2015-2020 entitled "Statistical Learning for Earth Observation Data Analysis" (SEDAL),, under the direction of Prof. Gustau Camps-Valls.

Application details
- Deadline: Send your application no later than April 1st 2017.
- How? Send me: 2-pages CV, motivation letter, papers if any, and one recommendation letter or contact
- When? Preferred starting dates: June 2017
- How long? 3 years contract
- How much? Salary according to UV scales including social security, health insurance benefits, and travel money
- Where? Valencia, Spain, Mediterranean city, nice weather, hike and beach. Excellent cost-of-living index = 55

- Before applying: Informal inquiries may be addressed to Prof. Dr. Gustau Camps-Valls,
- Ready to apply? Send your dossier in one single PDF to, subject: "SEDAL application"

The project and job description
We aim to develop the next generation of statistical inference methods to analyze Earth Observation (EO) data. Machine learning models have helped to monitor land, oceans, and atmosphere through the analysis and estimation of climate and biophysical parameters. Current approaches, however, cannot deal efficiently with the particular characteristics of remote sensing data. We will develop advanced regression (retrieval, model inversion) methods to improve efficiency, prediction accuracy and uncertainties, encode physical knowledge about the problem, attain self-explanatory models, learn graphical causal models to explain the complex interactions between essential climate variables and observations, and discover hidden essential drivers and confounding factors in Climate/Geo Sciences.

Highly motivated researchers with a degree in computer science, statistics, machine learning, electrical engineering, physics, or mathematics are encouraged to apply. All candidates should have a solid understanding and knowledge of machine learning and statistics, and being particularly interested in remote sensing and geoscience problems. The thesis will address problems in regression, graphical models and causal inference. Good programming skills (Matlab/Python/R/C++), a critical and organized sense for data analysis, as well as maturity and commitment, strong communication, presentation and writing skills are a big plus.