Open Positions

We are looking for a student assistant (HiWi) to support us with our teaching activities, particularly in the Biomedical Signal Processing lecture . The position starts in October 2026 and is available until the end of 2027. Please include your current Transcript of Records with your application.

Application: 

Please provide a short CV and transcript of records to finja.beermann@fau.de and daniel.fenzel@fau.de

Thesis description: 

Electrical stimulation is a powerful tool for investigating neural control of movement. A fundamental yet incompletely understood aspect of spinal motor control is recurrent inhibition, a feedback mechanism mediated by Renshaw cells that modulates the excitability of motor neurons and plays a key role in regulating muscle force and coordination. 

This thesis focuses on the quantification of recurrent inhibition in three muscles of the quadriceps group using paired-pulse electrical stimulation. By applying two precisely timed stimulation pulses, it is possible to probe the inhibitory influence on motor neuron excitability and thereby gain insight into the underlying neural circuitry governing thigh muscle coordination. 

The student will independently plan and conduct experimental measurements on human subjects, acquire the necessary technical knowledge in stimulation and electrophysiological recording, and develop a software interface for the acquisition and analysis of stimulation-evoked reflex responses.  

Requirements: 

• Preferred study programs: Medical Engineering, Data Science or any other comparable study program 

• Proficient Programming skills (preferably Python) 

• Basic knowledge of neurophysiology, electrophysiology, or biomedical signal processing 

• Ability to work independently and proactively learn new experimental techniques 

• Strong organizational and time management skills 

Supervisor: 

Finja Beermann, M.Sc 

Application: 

Please provide a short CV,  transcript of records and preferred start to finja.beermann@fau.de 

Thesis description:

Understanding how the human nervous system controls muscle activity is a central question in neuroscience and biomedical engineering. Intramuscular electromyography provides a unique opportunity to study motor unit activity with high spatial and temporal resolution, enabling deeper insights into neural control strategies.

This thesis investigates motor unit synergies based on intramuscular electromyography recordings from the quadriceps. The project is based on an existing dataset and focuses on uncovering patterns of neural activation across different muscles and movement tasks.

A central goal is to analyse how the nervous system coordinates multiple muscles to perform functional tasks. By applying statistical and data-driven methods, the project aims to identify underlying structures and coordination patterns in motor unit activity, contributing to a better understanding of neural control mechanisms.

Requirements:

• Preferred study programs: Medical Engineering, Computational Engineering, Data Science or any other comparable study program
• Proficient Programming skills (preferably Python and MATLAB/Simulink)
• Interest in neural control of movement and biomechanics
• Ability to work independently and competent time management skills

Supervisor:

Finja Beermann, M.Sc.

Application:

Please provide a short CV and a transcript of records to finja.beermann@fau.de

References:

Dernoncourt, F., Avrillon, S., Logtens, T., Cattagni, T., Farina, D., & Hug, F. (2025). Flexible control of motor units: is the multidimensionality of motor unit manifolds a sufficient condition? J Physiol, 0, 1–19. https://doi.org/10.1113/JP287857#support-information-section

Del Vecchio, A., Marconi Germer, C., Kinfe, T. M., Nuccio, S., Hug, F., Eskofier, B., Farina, D., & Enoka, R. M. (2023). The Forces Generated by Agonist Muscles during Isometric Contractions Arise from Motor Unit Synergies. The Journal of Neuroscience, 43(16), 423–507. https://doi.org/10.1002/cphy.cp010211

We are offering a thesis project focused on the integration of retraining across sessions as well as online model recalibration during real-time cursor control based on sEMG data recorded from legs and forearm. 

Tasks:

• Review relevant literature
• Explore performance degradation and ways for model recalibration
• Integrate validated findings into existing applications 
• Conduct experiments and evaluate performance

Requirements:

• Interest in biosignal processing and neural interfacing 
• Very good programming experience in Python (experience with PySide6, SciPy, PyTorch/Scikit-learn is a plus)  
• Solid knowledge of classical machine learning and deep learning 
• Ability to work independently and systematically 

Supervisor:

Amin Olamazadeh, M.Sc.; Annika Ritter, M.Sc.

Application:

Please provide a short CV and transcript of records to nsquared@fau.de.

Position description:

This thesis project investigates whether high-density surface electromyography (HD-sEMG) can detect regional activation differences within the quadriceps muscles, specifically between proximal and distal regions of the vastus medialis and vastus lateralis.

Previous work using intramuscular EMG (iEMG) demonstrated that selective or “split” activation between these regions is possible in most subjects. However, other studies using conventional surface EMG (sEMG) were not able to detect such regional differences.

The aim of this project is to directly compare iEMG and HD-sEMG recordings obtained from the same anatomical locations during voluntary selective activation tasks. HD-sEMG grids will be placed over proximal and distal regions of both muscles, while intramuscular electrodes will be inserted at corresponding sites. Participants will perform controlled contraction tasks and attempt region-specific activation. The recorded signals will be analyzed to determine whether HD-sEMG can reliably detect regional activation patterns comparable to those identified with iEMG. The study will contribute to understanding the spatial resolution limits of non-invasive EMG methods and their suitability for investigating fine neuromuscular control.

Tasks:

• Review relevant literature on regional muscle activation and EMG methodology
• Design and conduct EMG experiments
• Record and preprocess iEMG and HD-sEMG signals
• Compare regional activation patterns between methods
• Perform statistical analysis and interpret results
• Document findings in a scientific thesis

Requirements:

• Interest in neuromuscular physiology and biosignal processing
• Basic knowledge of EMG signal analysis
• Programming experience in Python or MATLAB is advantageous
• Ability to work independently and systematically

Supervisor:

Daniel Fenzel, M.Sc

Application:

Please provide a short CV and transcript of records to daniel.fenzel@fau.de.

References:

• Voluntary Dissociation of Motor Unit Activity in the Vastii Muscles
• The Forces Generated by Agonist Muscles during Isometric Contractions Arise from Motor Unit Synergies
• The Volitional Control of Individual Motor Units Is Constrained within Low-Dimensional Neural Manifolds by Common Inputs

Motivation:

High-density electromyography (HD-EMG) bracelets worn around the forearm can decode hand gestures and individual finger movements with promising accuracy on the day of training, but performance drops significantly on later days. Solving this cross-day gap is the bottleneck between lab demonstrations and a practical prosthesis / human-machine interface.

Goal:

Develop and benchmark a fast unsupervised cross-day calibration pipeline that lets a single HD-EMG decoder stay accurate across recording sessions, without the user having to repeat the full training protocol every day.

You will build on an existing Python codebase (PyTorch + Qt GUI).

Tasks:

• Run and analyse multi-day recording experiments on multiple subjects
• Diagnose why current cross-day performance drops
• Design, implement and benchmark a new unsupervised cross-day calibration method
• Integrate the winning method into the existing GUI for live use

Requirements:

• Solid Python (numpy, PyTorch)
• Basic signal-processing background
• Interest in biomedical / neural interface applications

Supervisor:

Amin Olamazadeh, M.Sc

Application:

Please provide a short CV and transcript of records to nsquared@fau.de.

Thesis description: 

Neuromuscular research relies on precise and standardized reflex assessments. To improve this process, we have developed a novel, spatially adjustable pneumatic tendon tapper specifically designed for the lower limb. Controlled via a graphical user interface written in Python, this custom-built device features an integrated force sensor to measure the impact of the tap while simultaneously monitoring the applied compressor pressure. The primary objective of this Master’s thesis is to validate this newly developed system to ensure its accuracy and reliability for future biomechanical and neurophysiological studies. 

During this thesis, you will conduct both a technical and a physiological validation of the device. First, you will evaluate the system’s mechanical reliability by testing whether constant compressor pressures consistently produce the same tap forces, and you will verify the internal force sensor’s accuracy by comparing its readings against an external reference sensor. In the second phase, you will validate the tapper in vivo by eliciting the patellar tendon reflex. Because the tapper is integrated into an existing 3-axis knee dynamometer, you will quantify the reflex response by measuring the resulting knee extension forces and correlating this mechanical output with electromyography (EMG) activity recorded from the thigh muscles. 

Requirements: 

• Preferred study programs: Medical Engineering, Computational Engineering, Mechanical Engineering, or any other comparable study program 

• Proficient programming skills (preferably Python or MATLAB), especially for data analysis 

• Basic micro controller knowledge 

• Ability to work independently and competent time management skills 

Supervisors: 

Yannick Finck, M.Sc. 

Application: 

Please provide a short CV and a transcript of records to nsquared@fau.de. 

Thesis description: 

Hand function in childhood forms the foundation for lifelong independence and
participation. Children with early unilateral brain lesions often develop severe paralysis of
the contralateral hand. Despite the lack of visible movement, recent studies show that
neural activity persists during attempted movements — too weak, however, to generate
functional motion.

Using high-density surface electromyography (HD-sEMG), it is possible to capture residual
signals in the paralyzed forearm. The goal of this project is to develop a children-optimized
classification pipeline for hand gestures to decode the motion intention in real-time and
applying the classification pipeline on an exergame to enhance the training motivation.

Tasks: 

• Support data acquisition for surface HD-EMG
• Develop a children-optimized model
• Apply the model into an exergame
• Test the approach together with children

Requirements: 

• Basic knowledge how to deal with data

• Basic knowledge in Deep and Machine Learning

• Proficient German language skills as you will interact with children

• Strong interest to work with children
• Strong interest in neurorehabilitation and neurophysiology
• Ability to work independently in an interdisciplinary research team

Supervisors: 

Pauline Wittermann, M.Sc. 

Application: 

Please provide a short CV and a transcript of records to pauline.wittermann@fau.de. 

Planning, designing and conducting a validation study of a custom-built 3d hand dynamometer. The device is designed to measure isometric finger forces in various directions. The validation study will assess the reliability of the dynamometer across multiple recording sessions for all five digits during both single and multi-digit contractions.

Tasks:

• Develop a comprehensive plan for the validation study
• Design and implement experimental protocols to evaluate the dynamometer’s performance
• Collect and analyse data to assess the reliability and accuracy of the dynamometer 
• Document the study design, methods, results, and conclusions

Requirements:

• Solid python skills 
• Solid knowledge in CAD is beneficial  
• Experience with git is beneficial 
• Ability to work independently and sound time management skills

Supervisor:

Charlotte Pradel, M.Sc.

Application:

Please provide a short CV and transcript of records to nsquared@fau.de.

Planning, designing and conducting a validation study of a custom-built 3d hand dynamometer. The device is designed to measure isometric finger forces in various directions. The validation study will assess the reliability of the dynamometer across multiple recording sessions for all five digits during both single and multi-digit contractions.

Tasks:

• Develop a comprehensive plan for the validation study
• Design and implement experimental protocols to evaluate the dynamometer’s performance
• Collect and analyse data to assess the reliability and accuracy of the dynamometer 
• Document the study design, methods, results, and conclusions

Requirements:

• Solid python skills 
• Solid knowledge in CAD is beneficial  
• Experience with git is beneficial 
• Ability to work independently and sound time management skills

Supervisor:

Charlotte Pradel, M.Sc.

Application:

Please provide a short CV and transcript of records to nsquared@fau.de.