260 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "UNIV" uni jobs at Monash University
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Learning. Conference on Empirical Methods in Natural Language Processing (EMNLP'20). Hua, Yuncheng; Qi, Daiqing; Zhang, Jingyao; Qi, Guilin; Li, Yuan-Fang. Less is More: Data-efficient Complex Question
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Algorithms for Scalable Data Systems and Intelligent Analytics Unsupervised Music Emotion Tagging (Affective Computing) AI of Neural Connectivity for Biomarker and Treatment-Response Discovery Establishing
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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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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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faculty before beginning your application. For more information visit our website or contact our Graduate Research Admissions team on E: mgro-apply@monash.edu or T: +61 3 99051538 Please ensure that you
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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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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