300 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL" positions at Monash University
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events with the GOTO telescope network. Projects focussing on thermonuclear bursts will involve analysis of new and archival data from satellite-based X-ray telescopes, and running numerical models
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Semester 2, close in May. Applications for a placement in Semester 1, close in October. Exact dates will be communicated by the Unit coordinator. Information sessions to be held prior to the application
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existing tokenization frameworks, analyzing potential risks, and developing novel security protocols to protect sensitive data and ensure the integrity of tokenized assets. Applicants will investigate
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an electrical and computer systems engineering degree in the Faculty of Engineering. Total scholarship value $20,000 Number offered One at any time See details Farrell Raharjo Clive Weeks Community Leadership
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academics into its Software Systems and Cybersecurity and Data Science & AI Departments. The Department of Data Science & AI is seeking a Teaching & Research academic working in Large Language Models and
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assays, bacterial susceptibility testing, and related laboratory procedures. Supporting research operations and data integrity by managing experiment scheduling, collecting and analysing data, preparing
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analysis, or multi-omics integration, with strong competence in deep learning frameworks (e.g., PyTorch/TensorFlow) and data engineering for reproducible research. Familiarity with cloud/HPC workflows
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into action, where you harness data to challenge the status quo, modernise practices and embed smarter, more agile ways of working. Collaborating with senior leaders across eSolutions and the wider University
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will study the development, adoption, and implications of digital technology and insurance—such as tools for capturing individualised data about behavioural risk factors and automating enforcement
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, and their decisions can be confusing due to brittleness, there is a critical need to understand their behaviour, analyse the (potential) failures of the models (or the data used to train them), debug