Postdoctoral Fellow-Earth and Atmospheric Sciences

Atlanta, GA

Postdoctoral Fellow position in Seismic Laboratory for Imaging and Modeling (SLIM) at the School of Earth & Atmospheric Sciences at the Georgia Institute of Technology. The postdoctoral fellow will be part of a cross-disciplinary research team in (Computational Science & Engineering, and Electrical & Computer Engineering, (led by Felix J. Herrmann) addressing challenges of seismic monitoring of Geological Carbon Storage (GCS), wave-based and [inverse] problems, and machine learning.

The successful appointee will be expected to undertake the following:

• Research in the application of simulation-based Inference and stochastic programming to seismic monitoring of Geological Carbon Storage
• Mentoring of graduate students
• Involvement in our open-source software (see ) development
• Collaboration with researchers of ML4Seismic
• Present the research outcomes in project meetings, to our industrial partners, and at national and international conferences
• Prepare deliverable reports and journal publications.

A PhD (or close to completion) in Earth Sciences, or possibly in a related field with a focus on areas such as numerical simulations, numerical linear algebra, statistical machine learning, and Bayesian Inference.

A PhD (or close to completion) in Earth Sciences, or possibly in a related field with a focus on areas such as numerical simulations, numerical linear algebra, statistical machine learning, and Bayesian Inference; a strong knowledge and excellent skills in at least two of the following areas: wave-based inversion (PDE-constrained optimization and adjoint-state methods); machine learning and uncertainty quantification (conditional normalizing flows, variational Bayesian inference); learned surrogates (Fourier neural operators, physics informed neural nets); and data assimilation (Kalman filters, sequential Bayesian inference). The candidate is also expected to be well versed in computational methods and programming (Python or Julia are essential) with experience in (scientific) machine learning and/or high-performance computing.

• Cover letter
• Curriculum Vitae
• Research statement
• Publication list

For additional information about this appointment, please contact

Felix Herrmann

felix.herrmann@gatech.edu

Georgia Tech provides equal opportunity to all faculty, staff, students, and all other members of the Georgia Tech community, including applicants for admission and/or employment, contractors, volunteers, and participants in institutional programs, activities, or services. Georgia Tech complies with all applicable laws and regulations governing equal opportunity in the workplace and in educational activities. Georgia Tech prohibits discrimination, including discriminatory harassment, on the basis of race, ethnicity, ancestry, color, religion, sex (including pregnancy), sexual orientation, gender identity, national origin, age, disability, genetics, or veteran status in its programs, activities, employment, and admissions. This prohibition applies to faculty, staff, students, and all other members of the Georgia Tech community, including affiliates, invitees, and guests.

We understand that being diverse makes us better which is why we support a culture of respect and equal opportunity, and value diversity at the heart of what we do. We wish to increase the diversity of our workplace to underpin a dynamic and creative environment, and strongly encourage applications from women, underrepresented minorities, individuals with disabilities, and veteran. We welcome and will consider flexible working patterns. Candidates with the skills and knowledge to productively engage with diverse communities are encouraged to apply.

The successful applicant will be given the support and mentoring required to develop their academic career in this key position at the forefront of world leading research. Please note that this job description is not exhaustive, and the role holder may be required to undertake other relevant duties relevant to the appointment. Activities may be subject to amendment over time as the role develops and/or priorities and requirements evolve. A flexible working schedule may be required to meet all key duties and responsibilities. We will measure applicants based on the essential criteria, not by the key duties.

While GCS is a promising technology to inject large amounts of supercritical CO₂ into porous and permeable rock formations for long-term storage, there may be certain risks associated with this technology that call for reassurance of the public, regulators, and other stakeholders of its safety. Even for well-designed CO₂ injection projects with accurately established baselines, there always remains a risk that the CO₂ plume leaves the storage complex via e.g. focused fluid flows, porosity collapse, or changes in fracture networks. At the (located within ), and as part of the industry partners program , we are addressing these risks by embracing recent developments in simulation-based Bayesian inference-i.e., the task of deriving statistical information from a system based on in silico simulations-that allows us to develop low-cost, scalable, high-fidelity and uncertainty-aware seismic monitoring solutions for GCS. The research will be conducted at in collaboration with and , co-director of .