Stealth Startup
Graduate Intern - Computational Geophysicist
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Opportunity for longer-term or permanent role
Work directly with the founding team to develop novel methodologies for a geophysical monitoring startup for clean tech applications.
This is a computational geophysics role focused on seismic processing, wave propagation modelling, and inverse problems, with the opportunity to work closely with experimental sensing and field data. The initial internship is for a fixed term of 2 months, with the potential to become a longer-term or permanent role subject to performance, project milestones, funding, and mutual fit.
Role Overview
We are looking for a computational geophysicist to support the development and validation of geophysical monitoring methodologies. The role will involve working with seismic and other geophysical data, developing numerical models of wave propagation, and solving inverse problems to understand or infer properties of the subsurface. A key part of the role will be using tools like Devito to formulate and execute geophysical modelling and inversion problems. We are particularly interested in someone who can take a physical geophysics problem, abstract it into the appropriate mathematical and computational formulation, implement it in Devito, and use the resulting model or inversion to investigate the problem. There will also be opportunities to apply machine learning and data-science techniques to experimental and field datasets. You will work directly with the founding team, take meaningful ownership of technical problems, and have significant autonomy to investigate, develop, test, and improve computational methods.
Responsibilities
- Develop and apply seismic processing workflows to experimental and field data.
- Develop numerical models of seismic and other geophysical wave propagation.
- Formulate and implement forward and inverse problems using Devito.
- Translate physical geophysics problems into mathematical models and computational implementations.
- Develop and evaluate inversion methodologies and assess their sensitivity, stability, and limitations.
- Generate synthetic data and use it to validate modelling and inversion approaches.
- Process, visualise, and analyse experimental and field measurements.
- Develop Python tools and workflows for modelling, inversion, data processing, and analysis.
- Explore the application of machine learning and data-science methods to geophysical datasets.
- Compare computational predictions with experimental measurements and help identify discrepancies between models and observations.
- Document computational methods, experiments, results, and conclusions.
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Desired Skills & Experience
We are particularly interested in candidates with a background in geophysics, computational geophysics, applied mathematics, physics, or a related field. A bachelor's degree in a relevant discipline combined with a master's degree or equivalent research/project experience in geophysics, computational geophysics, geophysical imaging or a related area would be a strong fit.
Essential or particularly valuable experience includes:
- Seismic data processing and analysis.
- Devito for geophysical wave propagation modelling and/or inverse problems.
- A strong understanding of wave propagation and the underlying physics of geophysical problems.
- The ability to formulate a geophysical problem mathematically and translate it into a computational model.
- Python programming and scientific computing.
Experience with the following would also be valuable:
- Seismic inversion and imaging.
- Full-waveform inversion or related inverse methods.
- Numerical methods for partial differential equations.
- Optimisation and parameter estimation.
- NumPy, SciPy, Matplotlib, and related scientific programming tools.
- Machine learning and data-science methods.
- Processing and analysing large experimental or field datasets.
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What Matters Most
We are looking for someone who can think from the physics through to the computation. We are looking for someone who is:
- Curious and analytically minded.
- Strong in the underlying physics and mathematics.
- Comfortable moving between theory, numerical modelling, and real data.
- Systematic when investigating computational or modelling problems.
- Comfortable prototyping and iterating on new approaches.
- Able to work independently and take ownership of problems.
- Interested in understanding why a model or inversion behaves as it does, rather than simply running established workflows.
Experience with every tool or technique listed above is not required. Strong fundamentals, genuine curiosity, and the ability to learn quickly matter more than familiarity with a particular software package.
Internship & Future Opportunities
This is a full-time, 2-month internship working directly with the founding team during an important development and validation phase of the company's technology. Outstanding performance may lead to an opportunity for a longer-term or permanent role, subject to individual performance, project milestones, company funding, and mutual fit. This is an opportunity to work on technically challenging geophysical problems and contribute directly to the development of novel sensing technologies, from computational modelling and inversion through to experimental validation and field deployment.
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