Csiro Postdoctoral Fellowship in Fairness Research

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**The Opportunity**:

- **Do you have a PhD in machine learning, computer science, statistics or applied mathematics?**:

- **Keen to conduct cutting-edge machine learning research?**:

- **Join the CSIRO Data61 team for this exciting 3-year postdoctoral role**

CSIRO Early Research Career (CERC) Postdoctoral Fellowships provide opportunities to scientists and engineers who have completed their doctorate and have less than three years of relevant postdoctoral work experience. These fellowships aim to develop the next generation of future leaders of the innovation system.

CSIRO Early Research Career (CERC) Postdoctoral Fellowships provide opportunities to scientists and engineers who have completed their doctorate and have less than three years of relevant postdoctoral work experience. These fellowships aim to develop the next generation of future leaders of the innovation system.

Machine Learning (ML) is increasingly used to inform high-stake, human-centric decisions including credit scoring and sentencing in the judiciary system. But ML algorithms are well-known to exhibit biases disadvantaging people from certain ethnicities and genders. Fairness research has contributed more equitable algorithms and has increased our understanding on different bias sources. However, an understanding of how these bias sources combine to forge the overall bias is still lacking. This project aims to disentangle ML-bias into algorithmic and data bias focussing on intersectional subgroups. The techniques developed can then be used to analyse biases in threatened species management and human-machine collaboration.

The primary role of the CERC fellow will be to conduct cutting-edge research. The research team comprises CSIRO researchers Sevvandi Kandanaarachchi, Cheng Soon Ong (Director MLAI FSP), Cecile Paris (Director CINTEL FSP) and Iadine Chades, and Eirini Ntoutsi from Bundeswehr University, Munich (Full Professor for Open Source Intelligence). Collaborating with the research team the CERC fellow is expected write conference and journal papers and present their work at local and international conferences. The CERC Fellow will have opportunities to learn new methodologies by participating in relevant workshops and longer courses such as a summer school. We expect the CERC Fellow to learn and grow to become an independent researcher by the end of this project. We will embed the CERC Fellow in the rich Data61 ecosystem where the CERC Fellow will have a supporting and enriching experience.

**Your duties will include**:

- Investigating approaches to disentangle and mitigate ML bias in an intersectional context.
- Working independently and programming in relevant languages such as Python/R/Matlab/C++.
- Carrying out innovative, impactful research of strategic importance to CSIRO that will, where possible, lead to novel and important scientific outcomes.

**Location**: Clayton, Victoria with the opportunity to travel to Canberra, Sydney and Brisbane

**Salary**: AU$92k - AU$101k plus up to 15.4% superannuation

**Tenure**: Specified term of 3 years

**Reference**: 93478

**To be considered you will need**:

- A doctorate (or will shortly satisfy the requirements of a PhD) in a relevant discipline area, such as machine learning, computer science, statistics or applied mathematics. Please note: To be_ _eligible for this role you must have no more than 3 years (full-time equivalent) of postdoctoral research experience.
- The ability to effectively program in a relevant programming language such as Python (others such as R/Matlab/Julia/C++ are fine too).
- High level written and oral communication skills with the ability to represent the research team effectively internally and externally, including the presentation of research outcomes at national and international conferences.

For full details about this role please view the Position Description

**Eligibility**:
**Flexible Working Arrangements**:
We work flexibly at CSIRO, offering a range of options for how, when and where you work.

**Diversity and Inclusion**:
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