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algorithms and deep learning models. Have proficiency in Python in a Linux environment and development experience using Tensorflow or PyTorch. Have strong linear algebra and computer vision knowledge. Have
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microfluidic fabrication and experiments 3D printing machine learning. Demonstrated programming skills (Matlab, C++, or Python). Desired Demonstrated ability to work independently and to formulate and tackle
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Statistics for the Australian Grains Industry 3 (SAGI3). Investment. The University of Adelaide, in collaboration with Curtin University and The University of Queensland, is leveraging machine learning, data
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instance learning and weak supervision / spatial transcriptomics models to individualise tumour type, associated biomarkers and genomic characteristics to high precision. The resulting multipurpose machine
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science technologies, and this is a perfect training opportunity for those who is interested in machine learning, data mining, artificial intelligence, and bioinformatics. High-performance computing may
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models that can forecast the likely outcomes of current practices. The project aims to develop cutting-edge machine learning and statistical risk prediction techniques to predict each short-term, long-term