Research Associate - Uncertainty Quantification and Optimal Experimental Design for Smart Site-Characterization of Onshore CO2 Storage Sites

Heriot-Watt University - School of Energy, Geoscience, Infrastructure and Society

The School is seeking an exceptional postdoctoral research associate to work on developing smart site-characterization techniques for onshore CO2 storage sites. This project is part of the Horizon 2020 project ENOS (Enabling Onshore Storage in Europe) that will focus on onshore storage, with the demonstration of best practices through pilot-scale projects and field laboratories, integration of CO2 storage in local economic activities and creating a favourable environment for CCS onshore through public engagement, knowledge sharing and training. ENOS is an initiative of CO2GeoNet, the European Network of Excellence on the geological storage of CO2, and a result of its recognition of the need to support onshore storage as a priority in today’s context. CO2GeoNet is committed to facing the technical and societal challenges for CCS through coordinated research and the global dissemination of scientific knowledge on CO2 storage. 

In this context, developing cost-effective site characterization techniques under budget constraints is a key-enabling technology. Practically, designing optimal data-gathering plans require repeated evaluation of complex numerical simulations with massive computational requirements. Data-driven proxy models will be developed to replace the detailed computational models for managing the computational costs needed to solve this optimal experimental design problem. The challenges addressed in this project spans across a number of fields including: uncertainty quantification (UQ), machine learning (ML), and Bayesian experimental design for subsurface reservoirs.

The position requires collaboration within a multi-disciplinary research environment consisting of computational scientists, geologist and petroleum engineers conducting applied research in support of the ENOS project.  Specific responsibilities include:

  • Development of non-intrusive polynomial chaos (PC) response surfaces based on machine learning techniques.
  • Coupling the developed algorithms with research and commercial multiphase flow packages.
  • Development of Bayesian optimal experimental design algorithms (utilizing the developed proxy models) for smart site characterization of CO2-storage sites.
  • Perform large-scale case studies for on-shore CO2 storage projects managed by other partners of the ENOS projects.
  • Publish the research output in peer-reviewed scientific journals.

You must have a Ph.D. in Computational Science/Engineering, Hydrology, Petroleum/Civil Engineering with strong computational background.

Informal enquiries should be directed to Dr Ahmed Elsheikh (

Applications are particularly welcome from women and black and ethnic minority ethnic candidates, who are under-represented in academic posts at Heriot-Watt.

For application details and further information please go to:

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