Research Fellow, Data Scientist (Wearable Technologies, Digital Health & Artificial Intelligence) Job No.: 697562 Location: Clayton campus Employment Type: Full-time Duration: 3-years fixed-term appointment Remuneration: $123,138 - $146,228 pa Level B (plus 17% employer superannuation) Amplify your impact
Monash/Bayreuth Double PhD Degree Opportunity - Accelerating organic semiconductor discovery with generative AI, molecular dynamics and DFT Job No.: 698040 Location: Clayton (home) and Bayreuth (host) Study mode: Full-time Degrees: E8009.1 - Doctor of Philosophy (Monash
Company Description The project will be undertaken within the School of Information and Communication Technology at Griffith University, in connection with the TrustAGI research lab. The lab conducts research on trustworthy and responsible artificial intelligence, with interests spanning
Line of ServiceAdvisory Industry/SectorNot Applicable SpecialismAnalytics Management LevelDirector Job Description & SummaryAs a Director within PwC Australia’s Data & AI practice, you will help clients shape and deliver their most important data and AI transformations. You
PhD: Self-Evolving Agents: Continual, Trustworthy, and Resource-Efficient Agentic AI Job No.: 698514 Location: Clayton campus Employment Type: Full-time Duration: The scholarship may be held for up to 4 years (fulltime) for Research Doctorate (PhD) studies. Remuneration:
At GHD, we dont just believe in the power of commitment, we live and breathe it every day. That’s why we pledge to support and empower all our people to make a positive impact when working
Please submit your CV in English and indicate your level of English proficiency. Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not
Company Description We are currently looking for a PhD candidate to undertake research in anomaly detection and graph machine learning. The primary focus of this role is to develop foundation models and frameworks for detecting anomalous patterns across