/Compression for electrophysiology data

Compression for electrophysiology data

PhD - Leuven | More than two weeks ago

Efficient wireless transmission for implants

In the context of projects Wireless for Neurotech (WiNe) and Intranet of Neurons (IoN), we are aiming for continuous wireless transmission of neuronal data from a neural implant on the cortex to aexternal receiver. This means both energy and data transmission through the skull in a safe and effective manner. This PhD project is to be part of this mission, focusing on methods to make the data smaller in 2 ways: both lossless and lossy. The lossless compression preserves required waveform of action potentials (i.e. spikes) fully, for model building purposes. The lossy data stream will contain mainly spikes with required morphological features. Determining what additional features make most neurological sense for the lossy data stream (e.g. in terms of neural decoding), is part of the envisaged research.

We offer you a challenging, stimulating and pleasant research environment, where you can contribute to international research on applying artificial intelligence to a challenging health context. This research will be a collaboration between the Holst Center in Eindhoven, and the AI & Data department at imec Leuven.

Our ideal candidate for this position has the following skills:

  • You have a Master’s degree in mathematics or computer science, and electrophysiology/neuroscience.
  • Programming experience in Python/MATLAB is a must.
  • Knowledge in machine learning is a plus.
  • Experience of processing large dataset is a plus
  • Understanding of hardware paradigms is considered a plus
  • You are able to plan and carry out your tasks in an independent way.

Required background: Master’s degree in mathematics or computer science, and electrophysiology/neuroscience

Type of work: Hands on focused research & development of mathematical methods and code

Supervisor: Steven Latré

Co-supervisor: Yao-Hong Liu

Daily advisor: Kasper Claes, Yao-Hong Liu

The reference code for this position is 2024-096. Mention this reference code on your application form.

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