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approximation algorithms for deriving dual bound within a branch-and-bound algorithms. Other directions could use Machine Learning or new decompositions. This subject is generally quite open so it is important to
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teaching spaces, and international collaboration opportunities. Learn more about Monash . Join the pursuit of our purpose to build a better future for ourselves and our communities - #ChangeIt with us
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an inclusive and respectful workplace culture where everyone is supported to succeed. Learn more about Monash . Be part of our story. Work with us to #ChangeIt . Monash supports flexible and hybrid working
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in doing it well. Whether your background is in public health, social science, urban planning or project coordination –what really sets you apart is your willingness to get involved, learn quickly, and
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respectful workplace culture where everyone is supported to succeed. Learn more about Monash . Join the pursuit of our purpose to build a better future for ourselves and our communities – #ChangeIt with us
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Bednarz. "Explainable ai and reinforcement learning—a systematic review of current approaches and trends." Frontiers in artificial intelligence 4 (2021): 550030.
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to real-life data. The goal is to generate new knowledge in the field of time series anomaly detection [1,2] through the invention of methods that effectively learn to generalise patterns of normal from
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they bond in materials, but also develop transferable skills in scientific computing, data analysis and visualisation. "Machine learning for atomic-scale structure determination in thick nanostructures" (with
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of generative AI. Essential Skills and Experience A background in a relevant field such as behavioural science, cognitive science, data science, psychology, human-computer interaction, law, or a related
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and develop your career in exciting ways. This is why we champion an inclusive and respectful workplace culture where everyone is supported to succeed. Learn more about Monash . Join the pursuit of our