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career progression opportunities via the academic promotions process. About You Completion or near completion of a PhD in the discipline area In-depth understanding of fundamentals of machine learning and
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: Completion or near completion of a PhD in either machine learning or a related area, OR HCI, human factors, psychology applied to AI, or a related area. Required: An emerging profile in research in the area of
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You Completion or near completion of a PhD in the computer science or relevant field. Experience with deep learning frameworks (e.g., TensorFlow, PyTorch) and familiarity with Multimodal Foundation
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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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) 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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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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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