Thesis Code: 22006
Thesis Type: Master Thesis in Computer Engineering, Biomedical Engineering, Mathematical Engineering, Computer Science
Research Area: Advanced Computing & Applications
Requirements
- MS students in for Computer Engineering, Biomedical Engineering, Mathematical Engineering, Computer Science
- Experience with at least one between Python and C/C++
- Depending on the specific sub-topic, one of the following skills may be valuable: knowledge of Deep Learning algorithms and data science frameworks (Keras, Pytorch, Pandas), reinforcement learning, and embedded software.
Description
The work fits in the context of the B-Cratos H2020-FET project (https://www.b-cratos.eu), where a machine learning-based methodology is being developed to translate electric signals recorded by brain-implanted electrodes, into meaningful commands for a robotic hand, while tactile feedback from an electronic skin is sent back to the brain to provide sensory stimulation.
Several sub-topics are available to explore depending on the interests of the candidate, the details of each can be further discussed after a first contact:
- Decoding of neural signals recorded from intra-cortical brain implants with deep learning and reinforcement learning
- Deployment of deep learning models on low-power hardware
- Development of the low-level code for the acquisition of the tactile feedback
The duration of the thesis work is expected to be around 9 months, adjustable based on the specific needs and skills.
Contact: Send CV to paolo.viviani@linksfoundation.com specifying the thesis code and title.
