PHD IN ADAPTIVE DATA ASSIMILATION IN SUBSURFACE SYSTEMS WITH ENSEMBLE TRANSPORT

Challenge. Advancing the energy transition requires understanding complex subsurface processes.
Change. Developing an innovative nonlinear data assimilation strategy that can adapt itself to different natural systems.
Impact. This method will permit better forecasting of safer operations and their consequences in the subsurface and beyond.

JOB REQUIREMENTS

• You hold a Masters degree in geoscience, geophysics, applied mathematics/statistics, or a similar field.
• You thrive on systematic, code-based research, and have an aptitude for abstract or mathematical thinking. We do not expect prior familiarity with triangular transport.
• You have a keen interest in data assimilation and machine learning, and are excited to explore their applications to geoscience problems.
• You have experience with Python or a similar programming language.
• You are a highly motivated and self-driven researcher, capable of working both independently and as part of a team.
• You have an excellent command of written and spoken English.

Your work will play a central role in advancing the adaptation part of our proposed data assimilation framework, in collaboration with a colleague PhD working on Conditional Independence. Your main tasks will include::

• Familiarize yourself with the theoretical and practical aspects of triangular measure transport, data assimilation, and subsurface applications.
• Explore strategies to adapt triangular maps to different environmental systems. This will involve concepts from information theory, hypothesis testing, and monotone functions.
• Co-develop a scalable, adaptive algorithm for nonlinear data assimilation based on triangular transport.
• Apply the algorithm as part of the proposed data assimilation framework to analyze the properties and uncertainties of a real geothermal operation.