Research Associate in Spatio-temporal Ml

3 months ago

Sydney, Australia University of New South Wales Full time

**Job no**: 528076
**Work type**: full time
**Location**: Sydney, NSW
**Categories**: Post Doctoral Research Associate

**The Opportunity**

The School of Computer Science and Engineering has an opportunity for a **Research Associate**. In this role you will conduct research in the field of Machine Learning, Fairness and Bias in AI, and Spatio-Temporal Forecasting for a collaborative project between researchers in the USA and Australia, that is funded by the U.S. NSF and CSIRO. The project, led by universities in both countries, aims to reduce bias in AI models used for predicting disease spread to improve pandemic response. Researchers will study how biased data affects AI models and explore fairness metrics to incorporate into AI optimization to mitigate bias.

This position will give you the opportunity to advance your scholarly research and professional endeavours both nationally and internationally. Your contributions will include publishing scientific papers and reports in international journals, actively participating in conferences and workshops, and collaborating closely with industry partners.

This role reports to Professor Flora Salim and has no direct reports.
- Salary, Level A, Step 6 - AUD$110,059 per annum + 17% superannuation
- Full time
- Fixed-term contract - 18 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.

**Skills and Experience**
- A PhD in a related discipline, and/or strong research experience in one or more of the following:

- Spatio-temporal modelling / data mining
- Deep learning, self-supervised learning, data-efficient learning
- Imbalanced learning, ML bias
- Experience with dealing with highly sparse and biased data
- Proven commitment to proactively keeping up to date with discipline knowledge and developments.
- Demonstrated ability to undertake high quality academic research and conduct independent research with limited supervision.
- Demonstrated track record of top-tier publications and conference presentations relative to opportunity, particularly in highly prestigious Q1 journals and conferences in AI, machine learning, and data mining, statistical ML, and/or spatial epidemiology research.
- Strong coding capabilities in running ML and data analytics experiments.
- Strong interests in the digital, technical, and societal aspects of public health research domain
- Demonstrated ability to work in a team, collaborate across disciplines and build effective relationships.
- Evidence of highly developed interpersonal skills.
- Demonstrated ability to communicate and interact with a diverse range of stakeholders and students.
- 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.

**To Apply**:
Applicants must have working rights in Australia and be able to be on site in Kensington regularly.

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

**Applications close**:11:55 pm (Sydney time) on Sunday 27 October 2024

Position Description

**Advertised**: 04 Oct 2024 AUS Eastern Standard Time
**Applications close**: 27 Oct 2024 AUS Eastern Daylight Time

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