Remote | Seismology & Computational Geophysics Specialist — $60–$75/hour

24-Mag•New York, NY

3d•Remote

About The Position

We are sharing a specialised part-time consulting opportunity for seismologists and computational geophysicists with graduate-level expertise in seismic modelling, inversion, imaging, scientific programming, and research-grade geophysical software. This role supports advanced computational research based on real seismology and geophysics workflows. Selected experts will design original graduate-level problems requiring scientific software, numerical simulation, data interpretation, experimental planning, and rigorous quantitative reasoning, then develop reference solutions and refine tasks through iterative testing.

Requirements

Graduate-level expertise in seismic modelling, inversion, imaging, scientific programming, and research-grade geophysical software
Master's degree, PhD, or equivalent research experience in Seismology, Geophysics, Computational Geophysics, Earth Sciences, or a closely related STEM discipline
Strong hands-on experience with computational seismology or geophysical modelling
Proven proficiency with at least one specialised tool such as SPECFEM, ObsPy, Pyrocko, SimPEG, pyGIMLi, SeisBench, EQcorrscan, or comparable software
Experience applying computational methods to real research or professional geophysics problems
Strong understanding of seismic wave propagation, inversion, imaging, tomography, or event analysis
Strong Python programming skills
Ability to design rigorous computational problems and independently verify solutions
Comfortable working in Linux and terminal-based environments

Nice To Haves

Research publications, open-source contributions, or substantial professional computational geophysics work are highly valued
Experience with scientific teaching, advanced problem-set design, computational reproducibility, or containerised environments is advantageous

Responsibilities

Create original graduate-level computational problems in seismology and geophysics
Develop tasks based on realistic scientific and research workflows
Design multi-step problems requiring numerical reasoning rather than straightforward calculation
Create reproducible tasks with clearly defined inputs, outputs, and validation criteria
Refine problem difficulty based on testing and technical feedback
Develop problems involving seismic wave propagation and numerical simulation
Create workflows for synthetic seismogram generation and interpretation
Evaluate modelling assumptions, boundary conditions, source parameters, and numerical behaviour
Design tasks requiring analysis of waveforms and propagation effects
Apply practical knowledge of computational seismology to realistic research scenarios
Create problems involving full-waveform inversion (FWI), travel-time tomography, and seismic imaging
Develop tasks requiring inference of subsurface properties from partial or simulated observations
Evaluate inversion strategies, model assumptions, convergence, and solution quality
Design workflows involving parameter estimation and uncertainty
Identify numerical or physical limitations that affect interpretation
Develop computational tasks involving earthquake or seismic-event detection and location
Work with moment tensor inversion and source-characterisation workflows where relevant
Design problems requiring interpretation of seismic arrivals, waveforms, and event parameters
Evaluate detection and location uncertainty
Create scenarios where multiple plausible interpretations must be distinguished through careful analysis
Apply hands-on expertise with tools such as SPECFEM, ObsPy, Pyrocko, SimPEG, pyGIMLi, SeisBench, EQcorrscan, or Fatiando a Terra
Develop reference workflows using specialised open-source geophysical software
Work with other scientific codes built in Python, C, C++, or Fortran where appropriate
Diagnose solver limitations, numerical edge cases, and implementation failures
Apply appropriate computational methods to real geophysical problems
Write problem setups, oracle functions, and solution validators
Use Python to create reproducible computational workflows
Verify numerical and scientific correctness of expected outputs
Document assumptions, parameters, dependencies, and validation logic clearly
Work comfortably within Linux and remote computational environments
Test computational tasks against advanced systems
Determine whether problems require genuine scientific reasoning rather than surface-level pattern matching
Identify tasks that are too easy, ambiguous, or computationally impractical
Refine prompts, datasets, constraints, and expected outputs until the intended difficulty is achieved
Maintain strong standards of scientific accuracy and reproducibility