Research Fellow in Multimodal Explanation of Causal Bayesian Networks

Monash University Faculty of IT

Australia

Research Fellow in Multimodal Explanation of Causal Bayesian Networks

Job No.: 606312

Location: Clayton campus

Employment Type: Full-time

Duration: 3-year fixed-term appointment

Remuneration: $99,147 - $117,738 pa Level B (plus 9.5% employer superannuation)

  • Be inspired, every day
  • Drive your own learning at one of the world’s top 80 universities
  • Take your career in exciting, rewarding directions

Monash University is a top 100 university located in Melbourne Australia. At Monash, you’ll have access to quality research and infrastructure, opportunities to collaborate internationally, and progress your research career. Our Faculty of IT is growing rapidly on its journey towards being one of the most renowned and highly regarded information technology research and technology centres in the world. We have an unsurpassed breadth and depth of expertise across the three Departments of Data Science & AI, Human-centred Computing and Software Systems & Cybersecurity. 

Monash University strongly advocates diversity, equality, fairness and openness. We fully support the gender equity principles of the Athena SWAN Charter and invite you to join us in celebrating women in STEMM.

The Opportunity

An exciting opportunity exists for a talented and ambitious research fellow to work on the ARC-funded Discovery Project “Improving human reasoning with causal Bayes networks: a multimodel approach”, led by researchers in the Department of Data Science and AI and the Department of Human-Centred Computing, in collaboration with cognitive psychologist partners in the UK. 

This project aims to improve human causal and probabilistic reasoning about complex systems by taking a user-centric, multimodal, interactive approach. The project explores new integrated visual and verbal ways of explaining a causal Bayesian network and its reasoning in order to reduce known human reasoning difficulties, and investigates how to reduce cognitive load by prioritising the most useful user and context-specific information.

Expected outcomes include novel AI methods that empower users to drive the reasoning process and strengthen trust in the system’s reasoning. Performance will be assessed in medical and legal domains, with significant potential benefits to end users from better, more transparent reasoning and decision making.

The Research Fellow is expected to design and implement new algorithms to identify and analyse Bayesian network features that people have difficulty understanding, model users/context, enable interaction, prioritise information, and generate verbal explanations. They will also lead human subject testing and evaluation. We are seeking someone with a doctoral qualification in a relevant field (computer science, AI, cognitive science or psychology) and demonstrated expertise in causal probabilistic models and causal reasoning, with knowledge of NLG, HCI or visualisation, an advantage.

This role is a full-time position; however, flexible working arrangements may be negotiated.

At Monash University, we are committed to being a Child Safe organisation. Some positions at the University will require the incumbent to hold a valid Working with Children Check.

For instructions on how to apply, please refer to “How to apply for Monash Jobs”.

Enquiries

Professor Ann Nicholson, +61 448 019 439, ann.nicholson@monash.edu 

Position Description

Download File Research Fellow

Closing Date

Thursday 28 May 2020, 11:55 pm AEST

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In your application, please refer to Professorpositions.com

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