PhD Scholarship for Causal Explanation with Bayesian Networks project
Faculty / Portfolio: Faculty of Information Technology
Location: Clayton campus
Scholarship: Pro-rata of $26,682 p.a. full-time for 3 years (plus $5,000 travel money per year) and tuition fees for 3 years for international student.
Please note, International students on a student visa must have health cover for the length of their visa while studying in Australia and this is not covered by the scholarship.
Expressions of Interest are sought from outstanding candidates to undertake PhD studies in the Faculty of Information Technology at Monash University.
This scholarship is attached to Associate Professor Kevin Korb’s project. The scholarship will be void if the applicant changes supervisor and/or project.
How to generate explanations from Bayesian networks is a long-standing problem that has attracted many different answers, for example, using mutual information (Suermondt, 1992, Explanation in Bayesian Belief Networks, PhD Stanford). Since those early days in the causal interpretation of Bayesian networks, the theory has flourished, especially in interventionist accounts of causation, for example in the work of computer scientists Judea Pearl (Causality, Cambridge Univ, 2009) and Joe Halpern, and also in the work of philosophers of science (James Woodward, Chris Hitchcock inter alia).
Within this tradition we have recently developed a causal information theory that can assist understanding and using causal Bayesian networks, combining mutual information with an interventionist theory of causality (e.g., Korb, Nyberg & Hope, 2011, "A New Causal Power Theory"). While causal information theory is a very promising tool, the theory itself is not fully developed and its precise application in making sense of causal explanations is not clear. Example of the former: multiple causes often interact with one another in their joint effects, but thus far accounts of the types of interactions and how to measure them are deficient. Example of the latter: causal explanation depends upon context (e.g., what conditions are assumed as a part of a causal query), but how to translate explanatory context into conditions or settings for a causal Bayesian network is not well understood. This project will aim to answer these questions.
This PhD project is funded with a full, independent scholarship and travel money and is part of a larger, international project aiming at using Bayesian networks to assist with argument analysis. The student will need to work within a team setting. A strong interest in philosophy of science, causal Bayesian networks and argument analysis is required. The student will need a good background in either artificial intelligence (Bayesian networks), philosophy of science or mathematics (Bayesian methods). Application is open-ended (not tied to the 31 October round), but we would like to start the project early in 2017.
Applicants will be considered provided that they fulfil the criteria for PhD admission at Monash University and demonstrate excellent research capability.
The successful applicant will have:
- a first class Honours degree or equivalent and/or a Master's degree by thesis in a relevant field
- strong written and verbal communication skills
- familiarity and demonstrated expertise in Bayesian networks
- research experience relevant to the research topic
- to meet Monash University's minimum English language proficiency requirements for entry into a higher degree by research program
Expressions of interest (EOI)
These should comprise:
- a cover letter that includes a brief statement of the applicant's suitability for and interest in the position
- a curriculum vitae, including a list of any published work
- a statement from the candidate explaining their interest in the field
- a statement of academic record, supported by scanned copies of relevant certified transcripts and grading systems of each qualifications
- completed Expression of Interest Form
Please email your EoI and supporting documents to the Project Officer at firstname.lastname@example.org.
Closing date for the EOI
Wednesday, 31 May 2017 11.59pm AEDT
For more information please click "Further Official Information" below.
This opportunity has expired. It was originally published here: