Postdoctoral Fellow in Machine Learning

**Job no**: 514194

**Work type**: full time

**Location**: Sydney, NSW

**Categories**: Post Doctoral Research Associate

**The Opportunity**

The School of Photovoltaic and Renewable Energy Engineering (SPREE) has multiple opportunities for Postdoctoral Fellows. As one of the Postdoctoral Fellows 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 their 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 - $99,325 to 106,237 per annum + 17% superannuation
- Full time
- Fixed-term contract - Initial 12 months with view for an additional 24 months
- 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:
**Skills & Experience**:

- A PhD in Computer Science or a related field.
- Thorough theoretical background in machine learning and deep learning.
- 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 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.
- Priority will be given to those who have theoretical background in semiconductor device physics.
- 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. Clear evidence of the desire and ability to continually achieve research excellence as well as the capacity for research leadership.
- Proven commitment to proactively keeping up to date with discipline knowledge and developments.
- A track record of significant involvement with the profession and/or industry.
- High level communication skills and ability to network effectively and interact with a diverse range of students and staff.
- Demonstrated ability to work in a team, collaborate across disciplines and build effective relationships.
- Evidence of highly developed interpersonal and organisational 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. Cooperate with all health and safety policies and procedures of the university and take all reasonable care to ensure that your actions or omissions do not impact on the health and safety of yourself or others.
- Align with and actively demonstrate the UNSW Values in Action: Our Behaviours and the UNSW Code of Conduct.

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

**Contact**:
Eugene Aves - Talent Acquisition Consul