Postdoctoral Fellow for Machine Learning applications for utility-scale PV

Apply now Job no:523884
Work type:full time
Location:Sydney, NSW
Categories:Post Doctoral Research Associate

The Opportunity

We are looking for machine/deep learning expert to join our team that is dedicated to improving the quality of photovoltaic systems and plants. This is a great opportunity for computer researchers to expand their capabilities in the area of renewable energy.

We, the School of Photovoltaic and Renewable Energy Engineering (SPREE), has an opportunity for a Postdoctoral Fellow. As a Postdoctoral Fellow you will contribute towards the research effort of UNSW in the field of photovoltaic (PV) devices and to develop their research expertise through the pursuit of defined projects relevant to your particular field of research.

This position will give you the opportunity to develop your scholarly research and professional activities both nationally and internationally through contributing to the writing of scientific papers and reports for international journals, participating in conferences and workshops, and actively engaging with industry partners.

The role of the Postdoctoral Fellow reports to Professor Ziv Hameiri and has no direct reports.

• Level A, Salary - $106,337 to $113,737 per annum + 17% superannuation
• Full time
• Fixed-term contract – Until December 2025 with view to extend
• Location: Kensington – Sydney, Australia

About UNSW

UNSW isn’t like other places you’ve worked. Yes, we’re a large organisation with a diverse and talented community; a community doing extraordinary things. But what makes us different isn’t only what we do, it’s how we do it. Together, we are driven to be thoughtful, practical, and purposeful in all we do. If you want a career where you can thrive, be challenged and do meaningful work, you’re in the right place.

This position is within the School of Photovoltaic and Renewable Energy Engineering (SPREE) that is internationally recognised for its record-breaking research in solar power (photovoltaics) and renewable energy. The PERC solar cell was first invented at UNSW in our labs in 1983 and today powers more than 85% of all new solar panel modules all over the world. SPREE’s work and people have changed the face of sustainable energy on the global stage, and we continue to be at the forefront of leading-edge research and development in the field of renewable technology as our economies transition away from fossil fuels.

For more information, please follow the below links:

https://www.unsw.edu.au/engineering/our-schools/photovoltaic-and-renewable-energy-engineering

https://www.acdc-pv-unsw.com/

https://www.unsw.edu.au/engineering/our-schools/photovoltaic-and-renewable-energy-engineering/our-research/research-activities/characterisation-defects-machine-learning

Skills & Experience:

• A PhD in Computer Science or a related field.
• Thorough theoretical background in machine learning and deep learning.
• Demonstrated experience in developing machine learning and deep learning algorithms for dynamic systems (sequential or time-series data), preferably for renewable energy (wind or photovoltaic) applications.
• Proven expertise in (and/or proven ability to learn if necessary):
  • One or more scientific programming languages, such as Python (preferred) or R, with a preference for functional style and algorithms experience.
  • One or more deep learning frameworks, such as PyTorch (preferred) or TensorFlow, with a preference for experience implementing SOTA models and training procedures from academic journal papers.
  • Development of data engineering pipelines (data aggregation and processing, database management, analysis, and visualisation).
  • Operating within Unix-based environments (headless servers, HPC clusters), with a preference for experience managing server infrastructure.
  • Collaborative software development using tools such as git/GitHub or equivalent.
  • Highly desirable: experience with generative representation learning models (VAEs, GANs, etc.) or similar unsupervised data modelling techniques.
•  A strong practice of maintaining an open-source code repository.
• A solid theoretical background in semiconductor device physics or substantial practical experience in electrical engineering, especially related to photovoltaic devices and systems.
• Demonstrated research excellence. Evidence of winning major international awards for research is highly desirable.
• Demonstrated track record in research with outcomes of high quality and high impact.
• Proven commitment to proactively keeping up to date with discipline knowledge and developments.
• Proven commitment to proactively keeping up to date with discipline knowledge and developments.
• Demonstrated ability to work in a team, collaborate across disciplines and build effective relationships.
• Demonstrated ability to communicate and interact with a diverse range of stakeholders and students.
• Evidence of highly developed interpersonal skills.
• An understanding of and commitment to UNSW’s aims, objectives and values in action, together with relevant policies and guidelines.
• Knowledge of health and safety responsibilities and commitment to attending relevant health and safety training.

Additional details about the specific responsibilities for this position can be found in the position description.

To Apply: Please click the apply now button and submit your CV, Cover Letter and Responses to the Skills and Experience. You should systematically address the Skills and Experience listed within the position description in your application.

Please note applications will not be accepted if sent to the contact listed below.

Contact:

Eugene Aves – Talent Acquisition Consultant

E: [email protected]

Applications close: 11:55 pm (Sydney time) on Tuesday 23rd April 2024

UNSW is committed to evolving a culture that embraces equity and supports a diverse and inclusive community where everyone can participate fairly, in a safe and respectful environment. We welcome candidates from all backgrounds and encourage applications from people of diverse gender, sexual orientation, cultural and linguistic backgrounds, Aboriginal and Torres Strait Islander background, people with disability and those with caring and family responsibilities. UNSW provides workplace adjustments for people with disability, and access to flexible work options for eligible staff. The University reserves the right not to proceed with any appointment.

Advertised:09 Apr 2024 AUS Eastern Standard Time
Applications close:23 Apr 2024 AUS Eastern Standard Time