Internship: AI/ML for Ionospheric TEC Modelling

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.