Characterization of movement kinematics using a machine learning method

Leuven
|
More than two weeks ago

Characterization of movement kinematics using a machine learning method

The Takeoka lab focuses on understanding how intrinsic capability of spinal cord circuits give rise to movement automaticity. We combine mouse genetics, virus-mediated neurocircuit manipulation and high-resolution kinematics to link neuronal activity of a specific population in the spinal cord to movements. To achieve this, a highly controled closed-loop setting needs to be achieved, detect movements (this is where you come in, to establish a machine learning method to characterize movement kinematics!), record neuronal activity in real time that would elicit electrical signals dependent upon one or both of inputs. You will be working closely with a postdoctoral fellow in the lab.

Type of project: Internship, Thesis, Combination of internship and thesis

Duration: Minimum of 6 months

Required degree: Master of Bioengineering, Master of Science, Master of Engineering Science

Required background: Physics, Computer Science, Bioscience Engineering, Biomedical engineering

Supervising scientist(s): For further information or for application, please contact: Aya Takeoka (Aya.Takeoka@nerf.be)

Imec allowance will be provided for students studying at a non-Belgian university.

Share this on

truetrue

This website uses cookies for analytics purposes only without any commercial intent. Find out more here. Our privacy statement can be found here. Some content (videos, iframes, forms,...) on this website will only appear when you have accepted the cookies.

Accept cookies