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School of Mathematics and Physics Analytics for the Australian Grains Industry (AAGI) Play a lead role in developing crop trait prediction solutions Utilizes physics-informed machine learning (PIML
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models. In-depth understanding of linear algebra and fundamentals of deep learning. Knowledge of Transferability in Machine Learning is desirable. Strong background in multimedia and computer vision tasks
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machine learning methods to predict protein structures, predict peptide and protein interactions, and design new peptide drugs and crop protection agents. The work is funded by the Australian Research
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, co-design workshops, cluster randomized controlled trials (RCTs), implementation science, data linkage, data science, machine learning and artificial intelligence. These are research focused positions
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bioinformatician/data scientist to use modelling and machine-learning approaches to learn from large data sets to inform further strain engineering or bioprocess optimisation rounds. Whilst the role is academic in
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) to use computational analysis and machine learning methods to interpret data relating to the screening and development of peptide-based drug leads with a particular focus on optimising their permeability
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statistical expertise. Experience in the area of digital health solutions, machine learning, and using real world evidence to inform decision making. In addition, the following mandatory requirements apply