Computational Seismology Expert - PhD

Obsidian; Berlin; Vor Ort; EUR 45.000 - 78.000; Vollzeit

Benefits dieser Stelle

Experience across multiple domains or

Benchmark/evaluation design

Scientific teaching/problem-set design

Computational reproducibility and容容容

Zusammenfassung

Obsidian is seeking a Computational Seismology & Geophysics Expert to design graduate-level problems that use real scientific software to run simulations, interpret results, and design experiments.

This role focuses on creating challenging, multi-step workflows and testing them against state-of-the-art AI models, refining until the target difficulty is reached. Strong Python and Linux skills, plus hands-on tool experience, are essential.

Qualifikationen

• Graduate-level training in a relevant STEM field (MS, PhD, or equivalent).
• Proven proficiency with at least one listed scientific software libraries via publications or open-source work.
• Strong Python skills for problem setups, oracle functions, and validators.
• Ability to work independently and refine problem designs based on feedback.
• Comfortable in Linux/terminal environments with remote compute sandboxes.
• Available for 15–20 hours per week.

Aufgaben

• Design original, graduate-level problems using real scientific workflows.
• Test problems against state-of-the-art AI models and refine difficulty.
• Develop problem setups, oracle functions, and solution validators in Python.
• Plan and run experiments to reveal hidden information and measure progress.
• Collaborate with researchers to ensure reproducibility and quality.

Kenntnisse

Python

Independent work

Problem design

Seismology knowledge

Ausbildung

MS/PhD or equivalent

Tools

SPECFEM

ObsPy

Pyrocko

SimPEG

pyGIMLi

SeisBench

EQcorrscan

Fatiando a Terra

Python

C

C++

Fortran

Jobbeschreibung

Computational Seismology & Geophysics Expert

About the Project

We're building a large-scale benchmark to test how well advanced AI systems can solve hard scientific and engineering problems. As a task designer, you'll create challenging computational problems that check whether AI can use real scientific software to do research-level work — running simulations, interpreting results, designing experiments, and uncovering hidden information from data.

This isn't a typical data-labeling job. You'll design original, graduate-level problems based on real scientific workflows, test them against cutting-edge AI models, and fine-tune them until the difficulty is just right.

What You'll Do

You'll create problems that require skilled use of specialized scientific software. Some will ask the AI to compute exact answers from a fully defined setup — testing whether it can correctly carry out complex, multi-step workflows. Others will be harder: the AI must plan a series of queries or experiments to uncover information that isn't directly visible, which means thinking strategically about what to measure, how to read partial results, and how to narrow down the possibilities efficiently.

Each problem goes through a testing loop against state-of-the-art AI models, and you'll refine it until it hits the target difficulty.

Domains & Tools We're Hiring For

We're especially interested in experts with deep, hands-on experience in:

Computational Seismology & Geophysics — hands-on experience with open-source, domain-specific computational tools such as SPECFEM, ObsPy, Pyrocko, SimPEG, pyGIMLi, SeisBench, EQcorrscan, or Fatiando a Terra, for seismic wave propagation and numerical simulation, synthetic seismogram generation, full-waveform inversion (FWI), seismic imaging, travel-time tomography, moment tensor inversion, event detection/location, or related computational geophysics workflows.

Experience with other open-source computational seismology or geophysics software will also be considered, including tools and scientific codes built with Python, C, C++, or Fortran.

What Makes a Strong Candidate

You have graduate-level expertise (MS or PhD preferred) in the domain above, with real hands-on experience using these tools — not just theoretical knowledge. You've written code using these libraries to solve actual research problems, and you understand where they break, what their edge cases are, and what makes a problem genuinely hard rather than just complicated.

Beyond domain expertise, the best candidates think like puzzle designers: building problems where the challenge comes from smart reasoning rather than raw computation, where several approaches seem plausible but only careful analysis reveals the right one, and where surface-level pattern matching won't get you to the answer.

Requirements

• Graduate-level training in a relevant STEM field (MS, PhD, or equivalent research experience)

• Proven proficiency with at least one of the listed scientific software libraries, shown through research publications, open-source contributions, or professional work

• Strong Python skills — you'll be writing problem setups, oracle functions, and solution validators

• Ability to work independently and refine problem designs based on feedback

• Comfortable working in a Linux/terminal environment with remote compute sandboxes

• Available for at least 15–20 hours per week

Nice to Have

• Experience across multiple listed domains or tools

• Familiarity with benchmark or evaluation design

• Background in scientific teaching or exam/problem-set design

• Experience with computational reproducibility and containerized environments