Internship: AI/ML for Ionospheric TEC Modelling
Posted 1 day ago by u-blox
Negotiable
Undetermined
Undetermined
Reigate, England, United Kingdom
Summary: The role is for an AI/ML intern at u-blox, focusing on modeling ionospheric Total Electron Content (TEC) behavior to enhance GNSS positioning performance. The intern will design and implement AI/ML models, analyze GNSS data, and collaborate with experts in the field. This position offers hands-on experience in satellite navigation challenges and exposure to GNSS receiver development. The internship is available in Reigate, UK, or Tampere, Finland, requiring candidates to be living or studying in either location.
Key Responsibilities:
- Design and implement AI/ML models to predict ionospheric TEC corrections for real receiver applications.
- Learn and model spatio-temporal patterns from GNSS measurements, TEC maps, and space-weather indicators.
- Explore and benchmark modern sequence modelling approaches (e.g., LSTM/GRU, Temporal CNNs, Transformers).
- Evaluate how learned corrections improve positioning accuracy and robustness under varying conditions (latitude, time-of-day, solar activity).
- Collaborate with experts in positioning algorithms, signal processing, and cloud-based data workflows to integrate models into realistic processing pipelines.
Key Skills:
- MSc student (or late-stage BSc) in Machine Learning, Data Science, Electrical Engineering, Physics, Aerospace/Geospatial Engineering, or similar.
- Strong Python skills and experience with PyTorch or TensorFlow.
- Interest in GNSS, space weather, or signal processing (prior deep domain expertise not required).
- Analytical mindset, curiosity, and ability to communicate technical findings clearly.
Salary (Rate): undetermined
City: Reigate
Country: United Kingdom
Working Arrangements: undetermined
IR35 Status: undetermined
Seniority Level: undetermined
Industry: Other
The ionosphere remains one of the largest error sources in GNSS. At u-blox, we are advancing next-generation receiver technologies that intelligently mitigate these effects using data-driven models. We are looking for an AI/ML intern to help model ionospheric Total Electron Content (TEC) behaviour and generate practical correction strategies that enhance positioning performance across diverse environments and operating conditions.
What you’ll work on
- Design and implement AI/ML models to predict ionospheric TEC corrections for real receiver applications.
- Learn and model spatio-temporal patterns from GNSS measurements, TEC maps, and space-weather indicators.
- Explore and benchmark modern sequence modelling approaches (e.g., LSTM/GRU, Temporal CNNs, Transformers).
- Evaluate how learned corrections improve positioning accuracy and robustness under varying conditions (latitude, time-of-day, solar activity).
- Collaborate with experts in positioning algorithms, signal processing, and cloud-based data workflows to integrate models into realistic processing pipelines.
Who we’re looking for
- MSc student (or late-stage BSc) in Machine Learning, Data Science, Electrical Engineering, Physics, Aerospace/Geospatial Engineering, or similar.
- Strong Python skills and experience with PyTorch or TensorFlow.
- Interest in GNSS, space weather, or signal processing (prior deep domain expertise not required).
- Analytical mindset, curiosity, and ability to communicate technical findings clearly.
What you’ll gain
- Hands-on experience applying AI to real satellite navigation challenges.
- Exposure to GNSS receiver development and large-scale positioning datasets.
- Mentorship from experts in navigation algorithms and signal processing.
- Opportunity to contribute to technology used in millions of connected devices worldwide.
This internship can be carried out in either Reigate (UK) or Tampere (Finland), you should be living and/or studying in either the UK or Finland in order to apply.