PhD Scholarship: Reinforcement Learning: Join an exciting research initiative aimed at understanding the principles behind the incredible speed and agility of cheetahs. This PhD scholarship focuses on leveraging inverse reinforcement learning techniques to decode how cheetahs achieve rapid accelerations and sharp maneuvers, with implications for advancements in robotics and artificial intelligence.
PhD Scholarship: Understanding Cheetah Locomotion using Inverse Reinforcement Learning
Designation
PhD Scholarship
Table
| Section | Details |
|---|---|
| Research Area | Robotics, Machine Learning, Biomechanics |
| Location | University College London (UCL), UK |
| Eligibility | Strong background in engineering, computer science, robotics, or related fields |
| Qualification | Experience or interest in machine learning, especially reinforcement learning; proficiency in programming (MATLAB or Python); passion for interdisciplinary research |
| How to Apply | Send your CV to amir.patel@ucl.ac.uk with the subject [DTP Application] |
| Last Date to Apply | 24 January 2025 |
Research Area
This research investigates cheetah locomotion using inverse reinforcement learning (IRL) methods, with the goal of applying these findings in agile robotics.
Location
The scholarship is based at University College London (UCL), collaborating with MathWorks and experts in various interdisciplinary fields.
Eligibility/Qualification
To qualify for the scholarship, candidates must possess:
- A strong background in engineering, computer science, robotics, or related fields.
- Experience or a keen interest in machine learning, particularly in reinforcement learning.
- Proficiency in programming, preferably in MATLAB or Python.
- A genuine passion for interdisciplinary research that bridges biology and robotics.
Description
This research project aims to explore and apply existing datasets of cheetah movements to extract reward functions that detail their high-speed maneuvers. It involves analyzing kinematic and kinetic data to identify the objectives that cheetahs optimize during rapid actions. The findings from this research will be implemented on the Unitree Go2 quadruped robot, enhancing its agility and enabling it to mimic cheetah-like movements.
How to Apply
Candidates interested in this transformative research opportunity are encouraged to submit their CV to Dr. Amir Patel at amir.patel@ucl.ac.uk, ensuring to use the subject line [DTP Application].
Last Date to Apply
The application deadline is 24 January 2025.
