26 parallel-and-distributed-computing-"Multiple" research jobs at Monash University in Australia
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Research Fellow - Environmental Informatics Hub Job No.: 680160 Location: Clayton campus Employment Type: Full-time Duration: 2 year fixed-term appointment (with the possibility of an additional 2
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for Health Economics is seeking a Level A Research Fellow to play a key role in an ongoing research program examining the effectiveness and cost-effectiveness of behavioural interventions, with a particular
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Journal (special issue on Kolmogorov complexity), Vol. 42, No. 4, pp270-283 Wallace, C.S. and D.L. Dowe (2000). MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions
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are seeking a Research Fellow to contribute to a project funded by the Australia Economic Accelerator (AEA) Ignite program, focusing on the design and integration of a novel point-of-care diagnostic platform
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computational tools for quantifying electromagnetic field distributions down to the fundamental atomic scale. The project will build on recent developments in inverse scattering methods, including ptychography
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effectively with colleagues, stakeholders and external partners. Excellent organisational skills with the ability to manage multiple tasks and meet deadlines. Why Join Us? This is a fantastic opportunity to be
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. We are currently seeking a Research Fellow with experience in AI and machine learning research and development, with a focus on any or all of following application areas: Computer vision Generative AI
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will support the Creative Destruction Lab (CDL) program by managing the application and admissions process, including engaging with applicants, coordinating interviews, and assessing submissions
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evaluate methods via experiments, benchmarking, simulation and/or real‑world data. The successful candidate will have: A PhD in Statistics, Data Science, Computer Science, Mathematics, or a related field
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the guidance of artificial intelligence techniques. The project will develop novel design processes that embed material behaviour within agent-based and machine learning computational design systems