PhD Scholarship in Human-Robot Interaction: Monash University, Australia, offers a fully funded PhD position for candidates interested in advancing the field of human-robot interaction through compositional multimodal agent models. This research project aims to develop social robots equipped with the cognitive, communicative, and social skills necessary to engage in meaningful, long-term interactions with humans.
Designation
PhD Research Scholar
| Details | Information |
|---|---|
| Institution | Monash University |
| Supervisors | Dr. Pamela Carreno-Medrano (Primary Supervisor), Dr. Hamid Rezatofighi, Prof. Dana Kulic |
| Funding | Fully Funded (Project-based scholarship) |
| Start Date | To be determined upon acceptance |
Research Area
Data Science and Artificial Intelligence
Location
Monash University, Australia
Eligibility/Qualification
- A strong academic background with expertise in machine learning applications for human-robot interaction.
- Practical experience in robot learning and user studies is highly desirable.
- Demonstrated proficiency in applying AI techniques to human-centered robotics problems.
Job Description
The selected PhD candidate will:
- Develop new algorithms for social robots to facilitate long-term human-robot interactions.
- Create compositional vision-language models enabling robots to understand and predict user behavior.
- Investigate how robots can reason about their actions and recover from errors during interactions.
- Conduct field studies to evaluate the effectiveness of the developed technologies.
How to Apply
Candidates interested in applying should send an expression of interest by email to pamela.carreno@monash.edu, quoting [MDFI Application] in the subject line. Applications should include:
- A cover letter
- Curriculum vitae
- Academic transcripts
- Sample publications (if any)
- A list of referees from whom recommendation letters may be requested.
Last Date to Apply
January 31, 2025
For further details and inquiries, please refer to the Monash University official website or contact the supervisory team directly.
