Open Positions

Please check our positions below. If you are interested in joining our lab, contact nsquared@fau.de with a short description of your scientific background and what you would like to do, e.g. bachelor/master thesis.

Master’s Thesis Opportunity: Neural Networks for Advanced Clinical Biosignal Analysis

Goals:

This master’s thesis focuses on developing neural network architectures for processing and interpreting complex electrophysiological signals during respiration.

The project aims to establish robust methods for real-time biosignal analysis and hardware synchronization, enabling integration into existing processing pipelines in collaboration with clinical partners.

The research will include validation studies to assess system reliability across different recording conditions and subjects.

The exact scope can be adapted based on the candidate’s interests and background.

Tasks:

  • Develop signal preprocessing pipelines for EMG data analysis and feature extraction during respiration
  • Design and implement neural network architectures for real-time biosignal pattern recognition
  • Evaluate and compare performance of different machine learning approaches including deep learning methods
  • Design and conduct experimental validation studies to assess system reliability and real-time capabilities under controlled laboratory conditions
  • Evaluate and validate the system performance in collaboration with clinical partners

Requirements:

  • Preferred study programs: Medical Engineering, Computer Science, or related fields
  • Strong programming skills in Python, with experience in machine learning and deep learning (neuromorphic computing is a plus but not required)
  • Familiarity with EMG signal processing and neuroscience concepts is advantageous
  • Strong analytical and problem-solving skills, with the ability to work independently and collaboratively

Supervisor:

Dr. Navid Bonakdar & Raul Sîmpetru, M. Sc.

Application:

Please apply with a cover letter, CV and transcripts to nsquared@fau.de.

Electromyography (EMG) is a measurement method used to assess muscle activity and activation through sensors placed on the skin. Modern algorithms leverage high-density EMG (HDsEMG) arrays to capture signals from muscles beneath the skin surface, enabling, e.g., gesture recognition in individuals with paralysis. The integration of HD-sEMG into textiles using printed electronics marks a significant innovation, facilitating accurate and rapid data acquisition for patients with motor impairments after neuromuscular lesions.

This project is conducted in collaboration with the Noxon GmbH in Munich.

Goals:

The goals of the project include recording intramuscular EMG (iEMG) concurrently with custom-designed HDsEMG textile electrodes to validate the identification of motor units in the Tibialis Anterior muscle of the leg, as well as in the forearm muscles during hand movements. Established decomposition tools will be utilized to extract unique motor units from the measured HDsEMG signals during muscle contractions at varying force levels.

Requirements:

  • Solid Matlab and Python Knowledge
  • Experience in biomedical signal processing
  • Experience in electrical design and CAD

Supervisor:

Dominik Braun, M. Sc.

Application:

Please apply with a cover letter, CV and transcripts to nsquared@fau.de.