Overview
The Human Technology Institute (HTI) at the University of Technology Sydney (UTS) is offering a PhD scholarship for a motivated candidate interested in advancing research and developing novel approaches in artificial intelligence (AI) and statistical machine learning that address complex, real-world challenges.
The Marine Ecosystems Research Mobilising AI and Data (MERMAID) project aims to enhance our understanding of the marine ecosystem. This full-time doctoral role is suited to a candidate with an interest in developing Bayesian models and methods for learning the structure of a Bayesian network. The successful applicant will contribute to research that informs innovative, evidence-based initiatives through strong partnerships with government, communities, or environmental stakeholders.
The doctoral student will conduct original research as part of a multidisciplinary team, including theory development, algorithmic design and implementation, data collection and analysis. They will be expected to engage with the wider academic community through presentations and seminars, participate in regular supervisory meetings, and contribute to the broader goals of the research project. The candidate will also be encouraged to develop their professional skills and academic profile throughout the course of the PhD.
MERMAID is a research program seeking to create a world-class, transformational research hub, harnessing the power of causal AI and environmental DNA (eDNA) data to revolutionize our understanding of the marine ecosystem. MERMAID is a multi-disciplinary partnership between philanthropy, leading mathematicians, computer scientists and marine researchers to forge a new digitally enabled and data-driven approach to discovery and modelling of eDNA.
MERMAID
- $40,500 per annum (tax exempt) living stipend for 3.5 years
- Applicants must study full time
- MERMAID is open to both Domestic and International students. International students must meet the UTS academic requirements found here.
Our research
Algorithmic development to explore high dimensional spaces of Bayesian networks for causal inference.
- Novel Bayesian models including mixture models of Bayesian Networks for explainable and interpretable structure learning.
- Dynamic Bayesian Networks and Bayesian hierarchical models based on individual education pathways.
- Bayesian Optimisation for decision-making under uncertainty – determining support initiatives and windows of chance to maximise positive outcomes using time series and dynamic Bayesian networks.
- Using community-informed prior distributions, quantifying uncertainty over success factors and priors for effective interventions and programs tailored for disadvantaged young students.
Qualifications
- Master’s degree or First Class Honours in Mathematics, Statistics, Computer Science, Computer Engineering, or related fields such as Physics, Engineering, or Robotics, with strong mathematical, modelling, or machine learning skills
- Strong foundation in mathematics and/or statistics knowledge
- Excellent written and verbal communication skills
- Ability to work effectively in a team and collaborate with others
- Good organizational and time management skills
- Analytical and critical thinking skills
- Proficient in programming languages such as Python, MATLAB or R
- Prior research experience or publication record is desirable
- Must meet the UTS PhD eligibility criteria, further information can be found here
How to apply
If you are eligible and interested in the role, please send the following to Danet Chapman, Program Manager at :
- A copy of your CV
- Academic transcripts
- Personal Statement related to project of interest
Seniority level
- Entry level
Employment type
- Other
Job function
- Research, Information Technology, and Education
- Industries
Referrals increase your chances of interviewing at University of Technology Sydney by 2x
Get notified about new Scholarship jobs in Sydney, New South Wales, Australia.
#J-18808-Ljbffr
📌 PhD Scholarship in Statistics, Machine Learning and AI for Applications toMarine Ecosystems
🏢 University Of Technology Sydney
📍 Sydney