Open Positions
Postdoc Position in Aerial Robotics
Job Duties and Responsibilities:
Our lab is seeking a Post-doctoral Research Associate in Aerial Robotics with a competitive salary and benefits. The initial offer can be extended in the following years based on available funding and the candidate’s performance. The successful candidate can be registered at Toronto Metropolitan University or the University of Waterloo. However, the work will mostly take place at Toronto Metropolitan University.
We are looking for an outstanding Postdoc to contribute to various aspects of the project on aerial robotics and manipulation. Please consider applying for the position if you have a strong related background. We especially seek individuals who have experience and knowledge in hardware and software design (ROS and writing highly optimized and efficient software in C/C++) and integration for robotic systems (particularly UAVs and aerial manipulators); proven track record in robot planning and control; and good knowledge in vision systems and potentially in visual servo control of robots. The candidates are expected to be independent with a self-reliant working style and willingness to learn about new and challenging topics. They are also needed to be outstanding team players with strong communication skills in English. The successful candidates will be actively involved in independent research, co-supervising students, grant application preparation, and teaching. The research will involve developing UAV and aerial manipulation planning and control methods, and SLAM. Also, the tasks will include software-hardware implementation of the methods, designing, and conducting both laboratory and field experiments.
How to Apply?
The following documents are needed:
(1) A motivation letter describing your research interests, your qualifications, and why you would be a suitable candidate;
(2) A detailed CV;
(3) Academic transcripts from your bachelor’s, master’s, and PhD degrees;
(4) Email addresses and contact numbers (preferably cell phone numbers) of at least three references (preferably your recent and PhD supervisors).
Interested applicants should send the necessary documents via email to RMAL@torontomu.ca. Applications should be submitted in PDF or word formats. In the subject line, please indicate: *PDF_UAV_2023*. Applications will be continuously evaluated until the position is filled. We thank all candidates for their interest; however, only shortlisted candidates will be contacted for the interview. Incomplete applications or applications not meeting the minimum requirements will NOT be considered.
Locations:
- RMA Laboratory, Department of Mechanical and Industrial Engineering, Toronto Metropolitan University, Toronto, Canada
- Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, Canada
- National Research Council Canada / Conseil national de recherches Canada (Civil Unmanned Aerial Systems), Montreal, Canada
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Multiple PhD Positions in Robotics and Control
[Opened on April 4, 2024]
Our lab, in collaboration with the University of Waterloo (external link, opens in new window) , National Research Council of Canada (external link, opens in new window) , and St. Michael’s Hospital (external link, opens in new window) is seeking PhD applicants to conduct research in one of the following areas: Aerial Robotics, Continuum Robotics, Visual Servoing, Medical Robotics, and Quantum Computing with full financial support. If you possess a strong background in at least one of the core research areas in our lab, namely robotics, control, and computer vision, please consider applying. The applicant’s competency will be evaluated based on their high-quality publications, prior projects, and grades in specific courses influential on the intended research. Prior hands-on experience related to the target research will be an asset. Naturally, qualified candidates will have profiles/skills from the following:
· Previous degrees in Electrical Engineering, Mechanical Engineering, Mechatronics/Robotics Engineering, Biomedical Engineering, Computer Engineering, Computer Science, or other related disciplines.
· Previous knowledge and experience in robot modeling and control, motion planning, robotic manipulation, vision systems and potentially visual servo control, and machine/deep learning applications in robotics and vision.
· Good background and experience in hardware and software design and integration for robotic systems.
· Good programming skills in C/C++, Python, or others. Experience with ROS is not required but is an asset.
· Minimum GPA of 3.33/4.33 (B+ or equivalent) in the master’s program (see International Equivalencies, if needed).
· English proficiency (Overall score in IELTS >= 7.0 or TOEFL IBT >= 93).
The successful candidates will be involved actively in research, co-supervising students, helping with grant application preparation, and teaching.
Additional Information
· The anticipated start date is Fall (September) 2024 or Winter (January) 2025. The application opens around mid-October 2023, and applicants selected after interviews will be encouraged to apply officially.
· RMAL is an Equal Employment Opportunity (EEO) employer. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, veteran status, disability status, or genetic information.
· Preference will be given to individuals from equity-seeking groups with equal qualifications. International candidates are highly encouraged to apply. We encourage all First Nations, Metis, and Inuit peoples or Indigenous peoples of North America, to self-identify in their applications and also reach out to Tracey King, Aboriginal HR Consultant for support during the selection process.
How to Apply?
The following documents are needed for the application:
· A detailed CV;
· A targeted motivation letter describing your research interests, your qualifications, and why you would be a suitable candidate for our position. Please be as specific as you can in describing what you will bring to the position.
· All academic transcripts from your previous degrees (Bachelor’s and Master’s degrees);
· Email addresses and contact numbers (preferably cell phone numbers) of three references (preferably your recent supervisors).
Interested applicants should send the necessary documents via email ONLY to RMAL@torontomu.ca. Applications should be submitted in PDF format. In the subject line, please indicate: “PhD_2024”. Applications will be continuously received and evaluated until the position is filled. We thank all candidates for their interest; however, only shortlisted candidates will be contacted for an interview. Incomplete applications or applications not meeting the minimum requirements will NOT be considered.
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Undergraduate or Graduate Research Assistant - UAV Design, Control, and Electronics
[Opened on April 4, 2024]
We are seeking a highly motivated and talented undergraduate or graduate student to join our dynamic team as an intern, focusing on UAV (Unmanned Aerial Vehicle) design, control, and electronics. This is an exciting opportunity to gain hands-on experience in cutting-edge technologies and contribute to innovative projects.
Qualifications:
· Knowledge of ROS/ROS2
· Experience with PX4 flight controller
· Experience with embedded hardware and motor controllers
· Experience in CAD design and software packages like SolidWorks or Solid Edge
Responsibilities:
· Design and development of UAV systems, including both hardware and software components
· Assist in the implementation of control algorithms for UAV navigation and trajectory planning
· Collaborate with a multidisciplinary team to integrate electronic systems into UAV platforms
· Troubleshoot and debug electronic systems
Interested individuals are encouraged to fill out (word file) this form (opens in new window) and send it by email to (RMAL@torontomu.ca) as soon as possible and attach a copy of their resume with the names and email addresses of three references and copies of their transcript(s).