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Job 18529f831b462b37

MScApplications Closed

Bharath Ramesh

Unknown Organization
Western Sydney University
Apply by Feb 27, 2024

Application deadline

Feb 27, 2024

Job location

Job location

Bharath Ramesh

Geocoding

Western Sydney University

Geocoding is still running and results will appear soon.

Source: legacy

Quick Information

Application Deadline

Feb 27, 2024

Start Date

Flexible

Education Required

See description

Experience Level

Not specified

Job location

Job location

Bharath Ramesh

Geocoding

Western Sydney University

Geocoding is still running and results will appear soon.

Source: legacy

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Job Description

The International Centre for Neuromorphic Systems, Western Sydney University, invites both domestic and international students to apply for the world’s first Master of Neuromorphic Engineering courses. We offer several programs, including a Graduate Certificate, a Graduate Diploma, a 1.5-year industry-oriented degree and a two-year research-oriented Master’s course in Neuromorphic Engineering. We seek dedicated, curious and open-minded scientists, engineers, physicists, electronics tinkerers, hardware and software hackers, and roboticists from diverse backgrounds. The course builds on the research background of our Neuromorphic Engineering and Event-Based Processing research staff. Successful applicants will receive significant mentorship. Mentors and course instructors will equip students with special digital vision and audition processing capabilities which are rarely taught at other Universities in the world. Mentors and instructors will provide students with opportunities to apply skills learned to practical projects which align with industry need. Although the post graduate courses will equip graduates with many in-demand machine-learning techniques, Neuromorphic Engineering researchers go beyond status-quo Machine Learning so that they can find solutions to issues that block progress in AI machine learning sensing and computer vision. Neuromorphic Engineering seeks to progress beyond failures in regular machine learning approaches as conventional approaches usually fail to generalise, are not environmentally sustainable, and are poorly suited to high-stakes time-critical low-powered applications.

Requirements

  • N/A
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