Research Scientist (Simulations and ML)

Research Scientist (Simulations and ML)

Six axes of motion. One quantum sensor. A lot of physics to account for. You will build the models that explain what motion does to a precision measurement.

The Role

A quantum gravimeter does not always operate on a stationary optical table. Motion and changing environments affect its measurements, and understanding those effects requires models that can be tested against real sensor data. As a Research Scientist (Simulations and ML), you will develop the physics and simulation methods behind our efforts to understand quantum sensors in motion.

Your work will span first-principles modeling, statistical signal analysis, experiments designed to resolve mismatches between models and measurements, and AI/ML pipelines for simulation and automation. You will also help connect transfer functions and motion models to sensor fusion and adaptive feedback systems. The goal is to understand noise and sensor behavior well enough to guide sensor design for demanding environments.

Technical Context

We cool atoms with lasers to near absolute zero to measure tiny changes in gravity. Yes, really. When the sensor is moving, a useful model must account for the physics of the measurement, the motion of the system and how noise appears in the data.

What You Will Do

• Develop and derive first-principles mathematical models for quantum gravimeters, including dynamic edge cases and approaches based on Feynman paths and optical Bloch equations, where applicable.
• Model how quantum-sensor noise degrades under complex six-axis motion, and derive transfer functions for changing environmental characteristics.
• Design and run experiments that distinguish real sensor behavior from modeling assumptions, resolving ambiguities between measurements and simulations.
• Analyze experimental signal characteristics using impulse responses, noise analysis, spectral kurtosis and cross-correlations; assess Cramér-Rao bounds and system observability.
• Support development of quantum-sensor simulation software for external environments; integrate transfer functions into motion libraries and contribute to sensor-fusion algorithms.
• Integrate AI/ML pipelines for automation and scaling, and design adaptive closed-loop filters that connect modeled behavior to sensor-control development.

What We Are Looking For

• Strong Python skills for scientific computing, simulation development and statistical analysis.
• Experience building or validating simulations of physical systems, such as high-energy physics, atomic physics, multibody dynamics or IMU sensor fusion.
• Strong foundation in classical mechanics, including inertial and non-inertial reference frames and rotating reference frames.
• Ability to analyze real-world experimental data, characterize noise and reason rigorously about model-to-measurement differences.
• Background in mathematics, physics, machine learning or control systems, with the analytical depth to work through unfamiliar physical problems.
• Strong first-principles reasoning, problem-solving skills and an interest in difficult technical questions that span physics, computation and real measurements.

Even Better If

• PhD in a relevant field OR minimum five years of relevant industry experience.
• Background in cold-atom interferometry and a strong foundation in perturbation theory.
• Experience with motion or localization simulation tools, such as Gazebo.
• Experience modeling physical systems in MATLAB or Mathematica.

What Success Looks Like

You can derive a model of a quantum sensor in motion, identify what it predicts about noise and signal behavior, and test those predictions against experimental data. Your analysis helps the team distinguish modeling error from physical effects and informs motion libraries, sensor-fusion methods, adaptive filtering and better sensor designs for demanding environments.

Working at Atomionics

Subsurface intelligence takes more than one discipline. People bring different expertise to the same problem, and what happens in one part of the system shapes the next.

The roles are different, but the goal is shared: make subsurface intelligence useful where it matters.

What We Can Offer You

Work across mathematical physics, scientific computing and experimental sensor data, with a direct connection between your models and how quantum sensors are developed and tested.

Build the technology that maps what no one else can see.