Senior Postdoctoral Researcher at Macquarie University
North Ryde, New South Wales, Australia -
Full Time


Start Date

Immediate

Expiry Date

13 Mar, 25

Salary

168275.0

Posted On

14 Feb, 25

Experience

3 year(s) or above

Remote Job

No

Telecommute

No

Sponsor Visa

No

Skills

Good communication skills

Industry

Computer Software/Engineering

Description

PRIMARY DETAIL

  • Salary Circa $146,324 to $168,275 (Level C) 17% superannuation
  • North Ryde location; Hybrid, full-time, 2-year fixed term role
  • Work closely with a team of industry and academic researchers
    Macquarie University are seeking a Senior Postdoctoral Researcher, to join the Faculty of Science and Engineering’s newly established Silicon Platforms Lab (SiPLab), on a full-time, fixed term basis for 2 years.
    You will lead a research team working on the application of software (AI/machine learning techniques), to improve the efficiency of advanced hardware and integrated circuit design. This role will undertake research into techniques that will enable x10 more efficient design of hardware systems.
    You will work closely with a team comprised of industry and academic researchers, made up of integrated circuit, board level, software, digital and systems researchers and engineers. You will support and mentor Masters and PhD students. Must be kind and a great team player.

Key responsibilities:

  • Work closely with hardware and integrated circuit designers to develop an AI/ML enhanced workflow
  • Understand where the most optimal points in the design flow are to apply these improvements
  • Research the best AI models and techniques for this domain
  • Build training sets and run experiments to train models
  • Implement software systems that enable x10 more efficient hardware design

How to Apply:
To be considered for this position, applicants must address the selection criteria below and attach your CV and a statement of research interests and achievements:

Selection Criteria

  • PhD in Physics, Electronics, Computer Science or a related field – you don’t need to know how to design chips, but must have an interest in learning
  • 3+ years working in Industry or a post doc role
  • Understanding of AI/machine learning, neural networks
  • Experience working with Python AI libraries (Pytorch, Tensorflow etc…)
  • Strong software development skills

Desirable Criteria

  • Experience with designing electronic circuits, either PCB or integrated circuits
  • Understanding of the hardware/chip development flow
  • Knowledge of electronics, or digital design (VHDL/Verilog) or analogue/RF design

Appointment Type: Full-time, fixed term for a period of 2 years.
We welcome International Applicants.
Salary package: From $ $146,324 to $168,275 (Level C) per annum, plus 17% employer superannuation. Appointment level will be determined based on successful candidate’s experience and skills.
Applications Close: Thursday 13th of March 2025 at 11:59pm (AEST).
Enquiries: Industry Professor Michael Boers
m.boers@mq.edu.au
We reserve the right to progress or decline an application prior to the application closing date.
If you’re already part of the Macquarie Group (MQ University, U@MQ, MQ Health, MGSM), you’ll need to apply through your employee Workday account.
Applications Close:
13/03/2025 11:59 PM
Diversity and Inclusion
Innovation and ingenuity thrive at Macquarie University when diversity, equity and inclusion take centre stage. At the University, we embrace a culture where diversity of background, experience and perspective are fundamental to our success.
We do not discriminate on gender identity, age, culture, disability, sexuality, Indigeneity, family and caring responsibilities or religion.
Flexible Work
At Macquarie, we believe by providing flexibility in when, where and how work is done, we can support our staff to manage their personal commitments, while optimising their work performance and contributions to the University.

Responsibilities
  • Work closely with hardware and integrated circuit designers to develop an AI/ML enhanced workflow
  • Understand where the most optimal points in the design flow are to apply these improvements
  • Research the best AI models and techniques for this domain
  • Build training sets and run experiments to train models
  • Implement software systems that enable x10 more efficient hardware desig
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