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areas. Key Responsibilities: To independently undertake research in computer vision and machine learning. To produce research reports and/or publications as required by the funding body
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tools such as R, Python, or MATLAB as well as relevant machine learning frameworks Experience in statistical data analysis, and expertise in areas such as experimental design, linear/nonlinear models
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) The Centre for Quantum Technologies (CQT) in Singapore brings together physicists, computer scientists and engineers to do basic research on quantum physics and to build devices based on quantum phenomena
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), stereo or high-speed imaging, and/or free-surface flows (including waves). However, an excellent candidate with a strong general experimental physics background can often learn quickly – the ability
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of machine learning, simulation-driven testing, and iterative calibration based on real-world datasets. Contribute to scholarly publications, technical documentation, and progress reports required by funding
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, specifically modelling burrowing behaviour and its impact on the skull. Responsibilities include conducting a range of computer simulations using discrete element and finite element methods, as
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environmental data Processing and analyzing large-scale remote sensing datasets from UAV, satellite, and ground-based sensors Leveraging artificial intelligence, e.g. machine learning, reinforcement learning
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spatial transcriptomics and imaging genomics projects, integrating bulk and single-cell RNA-seq datasets, and applying advanced statistical and machine-learning methods (AI/ML) to extract novel biological
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, and may utilise iterative algorithms, machine learning and high-performance computing. Through the Monash Centre for Electron Microscopy, opportunities exist to acquire large experimental datasets using
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. Proficiency in deploying and managing wildlife camera‑trap networks and processing large image datasets. Experience developing and validating machine‑learning and AI models for image object detection and