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, infer arbitrarily closely to any model underlying data. We endeavour to do the following: Apply MML to test datasets (degenerate motifs of 6-12 base pairs and with 1 to 2 variable nucleotides) Apply MML
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testing code, and learning from feedback. 🔍 Research Objectives Design an Agentic SWE Framework Model an AI system that combines reasoning, planning, and self-correction for software engineering tasks
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Levin Kuhlmann Research area Machine Learning We are seeking a highly motivated and innovative PhD student interested in exploring the opportunities for using AI to enhance personalisation of services and
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operational needs. Your work will support everything from budget development and revenue modelling to compliance, financial planning, and performance analysis. To be successful in this role, you will bring with
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on the development of a national-scale land use–transport interaction (LUTI) model. This model will be central to the project, generating critical indicators such as job accessibility and population
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, fabricate structures at the Melbourne Centre for Nanofabrication, and measure their optical and electrical properties. The successful candidate will have a PhD in Physics, Materials Engineering, or a closely
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and contribute to the preparation of manuscripts stemming from your work. If you have a PhD in organic chemistry, ideally with a background in protein chemistry or chemical biology you might be
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Relevant bibliography: - Chades, I., Carwardine, J., Martin, T., Nicol, S., Sabbadin, R., & Buffet, O. (2012). MOMDPs: a solution for modelling adaptive management problems. In Proceedings
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position as an EMBL Australia Group Leader. This opportunity places you at the forefront of one of the globe’s largest regenerative medicine hubs. Based at our cutting-edge Clayton campus and embedded within
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The proposed PhD project aims to build a machine learning/deep learning-based decision support system that provides recommendations on precision medicine for paediatric brain cancer patients based