Computational Geophysicist

About the job

Position: Full-time (100%) in-person with initial 1 year contract.

Location: Zurich, in-person (remote possible for first 3 months)

Start date: As soon as possible 

Location:  Zurich, Switzerland, with a preference for in-person work. For the right candidate, a fully remote arrangement within Europe may be possible for the first 3 months, with the expectation of relocation to Zurich thereafter. 

About us:

Storra is a deep-tech startup building software to optimise storage development and monitoring strategies for geological CO₂ sequestration projects. We develop and combine in-house and open-source software that sits at the intersection of reservoir modelling, geomechanics, reservoir geophysics, uncertainty quantification, machine learning and monitoring design for carbon storage. You will join a high-impact, fast-paced, purpose-driven environment, with the opportunity to help build next generation technology for the carbon storage industry. In addition, you will be joining a focused team of experts in CCS project development, reservoir modelling, geomechanics, MMV and risk assessments. 

NOTE: This is an opportunity to join the core cofounding team at a very early stage in Storra's growth. We expect candidates to have an entrepeneurial spirit, being open to both the benefits and risks of an entrepeneurial path.

Compensation:  Salary will depend on experience, profile and overall fit. As an early-stage hire, there may also be the opportunity for broader participation in the company, including the possibility to share in its long-term success.

Role Overview:

We are looking for a reservoir geophysicist / computational geophysicist whose core mission is to bridge reservoir simulations with synthetic seismic observations. The role is closely aligned with reservoir geophysics, quantitative interpretation, and time-lapse seismic reservoir monitoring, with a strong emphasis on forward modelling and simulation. You will develop and integrate modelling tools that translate subsurface state changes into predicted signals for time-lapse seismic and passive microseismic monitoring technologies. This work will enable us to quantify how well monitoring systems detect the CO₂ within the storage reservoir under uncertainty and thereby optimise seismic monitoring strategies for each storage site. There will also be a significant opportunity for applying and improving machine-learning algorithms for the development of fast surrogates of wave-propagation solvers. The role is likely to have a small client-facing component and to require travelling to conferences and storage sites. 

Your Responsibilities:

• Seismic forward modelling: Build and adapt numerical models for acoustic and elastic wave propagation. Use finite‑difference, finite‑element or spectral‑element methods and understand the assumptions required to make these models practical in an industrial setting. Experience with commercial or open‑source solvers in this domain is highly desirable.
• Time-lapse seismic and monitoring techniques: Support quantitative assessments of seismic monitorability by generating synthetic 4D seismic responses for surface seismic, VSP and DAS-VSP acquisitions, and model passive microseismic signals. Incorporate realistic acquisition geometries, noise levels and processing artefacts to approximate field conditions.
• Rock physics, petroelastic modelling and quantitative interpretation: Develop rock physics and petroelastic modelling workflows that link CO₂ plume evolution to seismic response. Support quantitative interpretation and reservoir-to-seismic integration workflows.
• Uncertainty and inversion: Run simulations across ensembles of geological realisations to assess detectability, quantify uncertainty and perform seismic network optimisations. Familiarity with inversion methodologies such as full-waveform inversion or joint inversion is an asset.
• Machine learning and reduced-order modelling: Develop, train and validate machine-learning, surrogate and reduced-order models that accelerate seismic forward modelling across large ensembles of geological and operational scenarios. Leverage analytical or semi-analytical approaches where appropriate and identify where high-fidelity solvers can be approximated.
• Practical software development: Write high-quality Python and C++ or Fortran code, leverage open-source tools, and build containerised workflows with Docker. Deploy and scale simulations on HPC and cloud resources using technologies such as MPI, OpenMP and GPU acceleration. Maintain documentation, tests and CI, and contribute to in-house libraries and workflows.
• Collaboration: Work closely with reservoir engineers, geologists, data scientists and clients. 

Who You Are:

• You enjoy taking ownership and helping also to shape the problem, not just executing a predefined task.
• You are excited by technically ambitious work, but you also know how to make pragmatic choices to move things forward.
• You work well in small, focused teams and bring a collaborative, low-ego approach to problem solving. At the same time, you are confident challenging weak assumptions or unworkable ideas in a constructive way.
• You are clear in your thinking and communication, and comfortable working across geophysics, modelling and software development.
• You are curious, resilient and motivated by the opportunity to build something meaningful in an early-stage environment.

Qualifications:

• PhD or MSc in geophysics, computational seismology, applied mathematics, physics or similar. A PhD is required for the position.
• Experience in reservoir geophysics, 4D seismic monitoring, or a closely related field.
• Proven experience developing and running seismic forward models (2D/3D acoustic/elastic) with finite‑difference, finite‑element or spectral‑element methods.
• Experience working with field seismic data, ideally including time-lapse seismic data for reservoir monitoring.
• Experience with time-lapse seismic analysis, seismic inversion, full-waveform inversion, and/or quantitative interpretation workflows would be highly valued.
• Solid understanding of time‑lapse (4D) seismic monitoring and VSP/DAS acquisitions, and some familiarity with passive microseismic monitoring.
• Strong coding skills in Python and at least one compiled language (C++, Julia, Fortran); experience with HPC (MPI, OpenMP) and modern software practices (Git, CI/CD, containers).
• Fluent written and spoken English

Application instructions / contact information

We are looking to fill the role as soon as possible, and applications will be reviewed on a rolling basis. Please include the following in your application:

• CV with relevant experience 
• Short and practical cover note/email (2-3 paragraphs) describing technical fit, appetite for working in an early-stage startup, and desire for relocation to Zurich versus remote work.
• Your expected gross annual salary range
• Links to relevant works, including publications, PhD thesis, GitHub, or similar.
• Contact information, including email & phone number
• Link to LinkedIn account (if available)