[robotics-worldwide] [jobs] Programmer at University of Ryerson

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[robotics-worldwide] [jobs] Programmer at University of Ryerson

Moh Gh
Position Announcement

Programmer/Developer in Machine Learning and Robotics

RMA Laboratory at Ryerson University, Toronto, Canada

(February 2017)

About the employer

The Robotics, Mechatronics and Manufacturing Automation Laboratory (RMAL)
was founded in 1997 and since then has been an active entity of Ryerson
University. We are an interdisciplinary laboratory bringing people and
projects related to Mechanical, Aerospace, Industrial, Biomedical,
Electrical-Computer Engineering, Mechatronics, MEMS, and Computer
Science. Our objective is to design and build in synergistic manner systems
for advancing robotics and manufacturing automation. We take a broad
approach of what constitutes good research and education, all measured by
their industrial and social impact. Our research works span from quite
applied to basic ones. The output from our research includes everything
from ground-breaking papers to ground-breaking prototypes. Our team of
industrial partners, faculty, graduates, and students creates unique
opportunities for furtherance of our education, research and technology
transfer goals. RMAL also welcomes inquiries from companies interested in
participating in our research and development works as industrial members.

Learn more at: www.ryerson.ca/~rmal

Job description

The opened position for a programmer/developer in robotics and machine
learning is available through NSERC’s I2I program. The project goal is
development and commercialization of a visual learning system for robot
programming by demonstration. The leaning system employs a vision system
for capturing tasks demonstrated by a human expert. The acquired
demonstrated motions are processed and transferred into executable robot
programs, which are utilized for reproduction of the demonstrated tasks by
the robot learner.

Job Duties and Responsibilities

The candidate is expected to contribute to the project for visual robotic
learning from human demonstrations. He/she will be expected to improve the
existing system for visual learning developed in RMAL. The tasks include:
modification of the current GUI for perception of human demonstrated
motions and execution by a robot, and implementation of robust methods for
trajectories generalization. A Microsoft Kinect sensor will be used for
perception of demonstrated actions. The GUI is created in ROS and in Visual
Studio using C#, and uses Kinect libraries and OpenCV libraries. The
candidate is expected to have knowledge of image features extraction and
tracking, as well as knowledge of machine learning algorithms, which are
going to be used for generalization of human motions. The ideal candidate
should be highly motivated and dedicated to the project. He/she will be
expected to work collaboratively with a postdoctoral fellow, and as a team,
they will put efforts into commercialization of the developed software
package for robotic visual learning.

Desired skills and experience

Qualifications Required

Minimum Qualifications Required:

   -

   Bachelor’s degree or a graduate degree in Computer Science
   -

   Minimum 3 years experience in programming with C++ or C# or ROS
   -

   Strong background in Machine Learning and Computer Vision
   -

   Ability to work independently, and in a team
   -

   Knowledge in Robotics will be considered as advantage


Additional Information:

   -

   The position will be for 8 months with the potential for extension,
   subject to satisfactory performance and availability of funding.
   -

   Anticipated starting date is as soon as possible.
   -

   Salary is competitive and depends on the previous experience of the
   candidate.
   -

   Preference will be given to Canadian residents.

Contact:

Interested individuals please contact Professor Farrokh Sharifi (
[hidden email]) by email with the SUBJECT: PROGRAMMER and provide the
following documents:

   1.

   Candidate’s CV and contact details
   2.

   Contact information of three references
   3.

   Academic transcripts
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