Senior ML Researcher – Geoscience (Physics‑Informed AI) in Cirencester

Senior ML Researcher – Geoscience (Physics‑Informed AI) in Cirencester

Cirencester Full-Time 60000 - 80000 £ / year (est.) No working from home possible
Client Server Ltd.

At a Glance

  • Tasks: Lead innovative ML projects at the intersection of physics and AI using geospatial data.
  • Company: Client Server Ltd., a pioneering firm in AI and geoscience.
  • Benefits: Equity options, hybrid work, and a competitive salary.
  • Other info: Flexible working environment with opportunities for professional growth.
  • Why this job: Shape the future of AI in geoscience and make impactful contributions.
  • Qualifications: PhD in ML/Physics with strong software and publication experience.

The predicted salary is between 60000 - 80000 £ per year.

Client Server Ltd. is seeking a Machine Learning Researcher to lead the intersection of physics and AI, applying ML to geospatial data and satellite imagery.

Based near Cirencester, you will shape the scientific direction and deploy production-ready models that provide explainable, validated insights.

The role requires a Ph D (or equivalent) in ML/Physics with a strong publication and production-grade software background.

Equity is offered; hybrid work includes three days remote per week.

#J-18808-Ljbffr

Senior ML Researcher – Geoscience (Physics‑Informed AI) in Cirencester employer: Client Server Ltd.

Client Server Ltd. is an excellent employer, offering a dynamic and collaborative work culture in the heart of London. With a focus on employee growth, the company provides ample opportunities for career progression and competitive compensation packages, including bonuses. Joining our team means being part of an innovative environment where your contributions to designing and optimising data platform components will be valued and impactful.

Client Server Ltd.

Contact Details:

Client Server Ltd. Recruitment Team

We think you need these skills to ace Senior ML Researcher – Geoscience (Physics‑Informed AI) in Cirencester

Machine Learning
Geospatial Data Analysis
Satellite Imagery Processing
Physics-Informed AI
Model Deployment
Explainable AI
Validation Techniques