178 information-security-"https:"-"https:"-"https:"-"DFG-TRR" PhD positions in Australia
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dissipation along the edges of the material. It is hoped that these sorts of materials will one day replace conventional silicon based electronics. This work is carried out within the Centre of Excellence
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Information: Please refer to the following links for more information about the Research Training Program and relevant Swinburne policy Contact Graduate Research Office HDRscholarships@swin.edu.au
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Scholarship Elizabeth Ann Crespin Scholarship Application is required. Check eligibility Key scholarship details Application status Open for applications Applications open 26 Jan 2026 Applications
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Status: Closed Applications open: 18/05/2025 Applications close: 9/06/2025 View printable version [.pdf] About this scholarship Description/Applicant information A PhD scholarship in
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imaging, based on absorption, provides good image contrast between high- and low-density materials, such as bones and soft tissue. However, it cannot distinguish subtle density differences between soft
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. An additional amount of up to $5,000 is available to support project costs, equipment or research-related travel for site visits, data collection, and/or conference attendance. Eligibility To be eligible
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Machine Learning for Image Classification. Eligibility You must: We would like you to have: sound knowledge of machine learning, computer vision and image processing strong programming skills. How to apply
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Skip to content info@ncwq.org.au info@ncwq.org.au Home About What we do Management Committee Advocacy Annual Reports Links & Resources Bursaries News NCWQ Newsletters Events Membership Affiliates
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polymer chemistry or tissue engineering. Strong skill set for data analysis and interpretation, coupled with excellent written and verbal communication abilities. Ability to work effectively in a
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injection into a reservoir saturated by water from core scale up to the numerical cell or a reservoir is a challenge. For immiscible two-phase flow in layer-cake reservoirs, the models for pseudo relative