TGS provides scientific data and intelligence to the global energy sector, enabling energy for all by unlocking vital, data‑driven solutions and knowledge. Through an extensive and diverse energy data library, advanced analytics, cloud‑based applications, and specialized services, we work in a way that is Passionate, Results‑Driven, Collaborative, and Responsible.
Overview
Serve as an individual contributor within TGS’s Rio de Janeiro R&D team, applying scientific machine learning and advanced analytics to complex subsurface and energy-related problems. This role focuses on hands-on model development, model maintenance, rigorous experimentation, and close collaboration with senior technical leaders. The position contributes to accelerating seismic processing with the use of AI/ML but also through rigorous use of data analytics to QC the results.
Key Responsibilities
Design, implement, and evaluate scientific machine learning models for subsurface and energy-related data.
Contribute to the development of large-scale representation learning systems, including supervised, self-supervised and weakly supervised approaches.
Conduct rigorous, hypothesis-driven experiments and clearly communicate results and limitations.
Collaborate closely with data scientists, domain experts, and cross-functional teams on framing ML based solutions.
Support the translation of research ideas into scalable production solutions and workflows.
Apply ML techniques to 1D, 2D, and 3D signal processing challenges, specifically targeting seismic processing steps.
Participate in external research activities, publications, or technical forums.
Key Competencies
Scientific Machine Learning: Strong understanding of ML applied to physical or scientific systems.
Deep Learning Expertise: Hands-on experience with modern architectures and training workflows.
Experimental Discipline: Ability to design controlled experiments and critically evaluate results.
Technical Collaboration: Works effectively within multi-lead, interdisciplinary environments.
Professional Maturity: Operates independently on complex technical problems while seeking alignment when appropriate.
Qualifications
PhD (or MSc with strong applied or research experience) in Machine Learning, Data Science, Applied Mathematics, Physics, Geophysics, or a related field.
Strong preference for candidates with experience in Wave Physics, Signal Processing, or Image-to-Image translation (e.g., ViT/U-Nets) applied to physical data.
Demonstrated expertise in modern machine learning through research, applied projects, or open-source contributions.
Experience working with complex or large-scale datasets.
Energy, geoscience, or scientific/industrial domain experience is a plus but not required.
Competence with Python and Pytorch is highly preferred.
If you meet the qualifications and are passionate about contributing to our team, we encourage you to submit your application by 04/13/2026.
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