17 artificial-intelligence-phd Fellowship research jobs at RMIT University in Australia
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Overview: Full-time, Fixed Term for 3 Years Salary Academic Level A6-B1 ($102,047 -$115,303 p.a.), depending on post-PhD experience 17% Superannuation and Flexible Working Arrangements Based
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or implementing deep learning approaches on existing clinical systems). Experience and interest in grant writing would be viewed favourably. To be successful in this position, you will have: A PhD in a relevant
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software (Prism, Origin Pro, Matlab etc) A good understanding of appropriate statistical analysis techniques Qualifications Mandatory: PhD or equivalent in relevant field. Please Note: Appointment to
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Indigenous Principal Research Fellowship for four years and salary commencing between Academic level D1-D4 (depending on experience) $40,000, per annum, research support funding for four years One PhD
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postgraduate research students. Ability to contribute to the teaching and learning program in a relevant field. Qualifications: Mandatory: PhD in Ecotoxicology or Environmental Science or in a relevant field
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higher degree by research candidates, staff and industry, together with a strong commitment to teamwork and multidisciplinary collaboration. Mandatory: PhD in a relevant field such as (but not limited
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in NLP. Completed a PhD or equivalent qualification or research experience in machine learning, natural language processing and image processing. Emerging track record and recognition for quality
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analysis. Interdisciplinary collaboration. Critical thinking. Qualifications and requirements The candidate should have a PhD in a topic relevant to regeneration and a track record of undertaking and
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and to produce high quality outputs. To be successful in this position, you will have: PhD or equivalent in relevant field. Demonstrated capability involving nanomaterials formulation (preferably lipid
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this position, you will have: PhD degree in relevant field Demonstrated knowledge of structural analysis of alloy components, Finite Element Modelling (FEM) and preferably morphology/topology optimisation