9 parallel-programming-"Computer-Vision-Center" PhD research jobs at Monash University
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machine/deep learning, computer vision, or applied statistics. Solid programming skills in Python and experience with deep learning frameworks (e.g., PyTorch or TensorFlow) Project funding Other Funding
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project is to use discrete optimisation techniques (e.g. Integer Programming) to design new algorithms for MAPF. An example of previous research in this direction was published in IJCAI. Required knowledge
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advanced immersive visualisation and visual analytics using Augmented and Virtual Reality environments in visualising and interacting with protein structures. Required knowledge Strong computer-science and
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Spectral Encoding. Develop advanced neural architectures (e.g., adapted Vision Transformers or 1D sequence models) capable of fusing multiple distinct analytical "views" (e.g., combining MS2 and NMR data
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Spectral Encoding. Develop advanced neural architectures (e.g., adapted Vision Transformers or 1D sequence models) capable of fusing multiple distinct analytical "views" (e.g., combining MS2 and NMR data
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learning. A publication or two in related areas will be a plus. the candidate should have strong Python/R <b>programming</b> skills. The candidate will work closely with experts in both machine learning and public health. Project funding Project based scholarship Learn more about minimum entry...
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learning. A publication or two in related areas will be a plus. the candidate should have strong Python/R <b>programming</b> skills. The candidate will work closely with experts in both machine learning and public health. Project funding Project based scholarship Learn more about minimum entry...
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, Artificial Intelligence and Machine Learning Python and/or R programming Data preprocessing, feature engineering and statistical analysis Supervised and unsupervised machine-learning techniques Deep
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, Artificial Intelligence and Machine Learning Python and/or R programming Data preprocessing, feature engineering and statistical analysis Supervised and unsupervised machine-learning techniques Deep