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Epilepsyecosystem.org as a way to bring researchers, code and data together from all over the world to help solve the problem of seizure prediction. Required knowledge Machine learning, AI, signal processing, dynamical
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developed and implemented such methods for a plethora of non-classical logics [2]. But how can we guarantee that the implementation is faithful to the theory? Indeed, how can we be sure that we have not made
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is like Ockham's razor, seeking a simple theory that fits the data well. It can also be thought of as file compression - where data has structure, it is more likely to compress, and the greater
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processing, or autonomous driving. Proficient coding skills in Python, preferably with hands-on experience on deep learning frameworks such as PyTorch, TensorFlow, or Keras. Project funding Project based
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Minimum Description Length: Theory and Applications, M.I.T. Press (MIT Press), April 2005, ISBN 0-262-07262-9. [Final camera ready copy was submitted in October 2003.] Dowe, D.L., J.J. Oliver and C.S
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). The reason for this is that the candidate will need to be trained in theories about humans and experimental methods. Meet H1E requirements for Monash FIT PhD entry.
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programming. An ability to code in C, C++ or Rust is also necessary. Candidates with experience or interest in column generation, cutting planes, polyhedral geometry and graph theory are especially invited
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Machine learning, dynamical systems theory, control theory, signal processing, network theory, neuroscience are all relevant and a student should have strong knowledge in at least one of these and a
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, and data protection Apply engineering principles, including version control (e.g., Git), CI/CD pipelines, and infrastructure-as-code (IaC) to ensure robust and repeatable deployments across both
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conditions (low back pain and diabetes). It will then co-create a theory-driven, evidence-based assessment rubric that will form the foundation of an online framework for assessing the reliability