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, including survival analysis, time-series techniques, causal inference approaches, and/or machine learning methods to large healthcare datasets. Prior experience mentoring or supervising graduate students
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modelling and machine learning for large and complex datasets. Have proficiency in Python and/or R for time-series and sensor data analysis. Have an interest in or experience in environmental exposure
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 2 months ago
machine-learning methods to investigate the deep-time controls on copper mineralisation. The role will involve developing reproducible computational workflows, generating predictive maps of copper
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functions to work properly. Please turn on JavaScript in your browser and try again. UiO/Anders Lien 1st March 2026 Languages English English English PhD Research Fellow in reinforcement learning
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candidate. The overarching theme will be the interplay of training data composition (e.g. different types and selections of data) and fine-grained evaluation in the development of large language models. LTG
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that are tailor-made to the research questions and data of CREATE. The successful candidate will conduct advanced methodological and psychometric research. Potential topics include (a) AI, machine learning, and
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-organization during pre-implantation development, in close connection with experimental data from live imaging and spatially resolved gene expression profiling. The work of the PhD fellow will be theoretical and
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conducting quantitative analyses or master game theoretic analysis. Experience with large language models, machine learning, and/or programming in R or equivalent programs is an advantage but not a requirement
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. This pivotal role sits at the heart of the Strategic Alliance between Open Door and Military and Emergency Services Health Australia (MESHA), strengthening national research capability and driving meaningful
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equations, stochastic partial differential equations, stochastic mean-field equations, stochastic control and filtering, stochastics for data analysis and machine learning. These areas will be prioritized