Intern, Spatially Sparse AI- video decoders

Summary

In this internship at the London AI Video Lab, the objective is to design computationally efficient video decoders in an AI-based video compression codec. Current AI-based video compression models outperform conventional codecs such as HEVC, VVC and AV1, but they require impractically high computational resources during decoding. The goal of the internship is to create efficient AI-based decoders that leverage spatial sparsity to reduce computational complexity and advance the deployment of end‑to‑end trained AI-based video compression models.

Duration: 5–6 months, starting January‑April 2026.

• State‑of‑the‑art analysis of existing solutions.
• Implementation of a computationally efficient AI‑based video decoder.
• Evaluation and reporting of results.

• MSc in Computer Science, Machine Learning, Mathematics, Physics or a related field.
• Proficiency in deep learning, computer vision, Python and PyTorch.

A spatially sparse AI‑based video model and its evaluation, along with the generation of patents and publications.

London, UK

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