Internship: AI/ML for Ionospheric TEC Modelling
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The Ionosphere and GNSS
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.
Role Overview
We are looking for an AI/ML intern to help model ionospheric Total Electron Content (TEC) behavior 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 modeling 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.
Qualifications
- 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 to apply.
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The Ionosphere and GNSS
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.
Role Overview
We are looking for an AI/ML intern to help model ionospheric Total Electron Content (TEC) behavior 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 modeling 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.
Qualifications
- 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 to apply.
