70 buidling-information-modelling Fellowship positions at The University of Queensland
Sort by
Refine Your Search
-
Research Fellow to engage in research on the area of data analytics, modelling, machine learning and process control to further their expertise and growing research profile in their discipline. The focus
-
intensive care data to develop risk prediction models, and translate models into clinical practice. The successful applicant will ideally have a growing research profile in data science, machine learning
-
Completion or near completion of a PhD in the discipline area Experience in application of mixed models in multi-variate and/or multi-dimensional scenarios Advanced skills in computer programming for data
-
information. federated learning, whereby a machine learning model can be trained by using data from different clinical sites but without requiring central data aggregation. The data used in this project will
-
of field monitoring (hydrological, chemical and geophysical monitoring), analysis of data and its use in models. Occasionally travel from Brisbane to the field sites in other parts of Australia. Communicate
-
, and marine spatial planning tools, and proficient in Python and R. Experience with multivariate statistics, modelling methods, Bayesian hierarchical models, spatial models, and time-series analyses is
-
and construction of these scaffolds will be informed by the development of multiscale computational models that characterise the biomechanics of the shoulder joint and the rotator cuff, in particular
-
collection, curation and data analysis Development, calibration, and validation of models for the minerals processing industry Contribute to progressing towards transfer of knowledge, technology, and practices
-
knowledge and experience in relevant empirical research methods, including Information Retrieval, Large Language Models, question answering, answer generation and ranking, abstractive summarisation
-
team and industry collaborators. Analyse multi-omics data to construct computational models of cyanobacterial metabolism. The models will be used to predict metabolic bottlenecks and generate genetic