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institutional policies. To be considered for this role, you must hold a doctoral qualification in operations research, operations management, business analytics, data science, machine learning, or a closely
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, artificial intelligence, machine learning and deep learning, and will support robotic field trials and data collection. An important component of the role is to coordinate and support a broad range of research
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machines and will be highly proficient in the use of manual and numerically controlled machining methods suited to specific construction tasks. You bring a proven track record of independently developing
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will include: Provide high quality, efficient administrative support to staff (e.g. exam preparation, e-learning support, course profile publication). Provide client focused, policy based administrative
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or hardware design (e.g. PC interfacing of cameras, scientific instruments, Arduino-based devices) Skills In system prototyping using CAD software (Autodesk Inventor), 3D printing, machined parts, and COTS
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an outstanding environment in which to develop innovative research in mathematical and statistical data science, with opportunities for collaborations with machine learning and bioinformatics researchers in
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demonstrated ability to communicate and interact with a diverse range of stakeholders and students. Demonstrated knowledge in Quasi-Monte Carlo methods and/or finite element analysis and/or machine learning is
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with top rankings in Victoria around student support, social equity, skills and employment, we are driven to make difference to every student and the community providing lifelong learning, skills, and
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postgraduate degrees by using our accelerated learning model. The College offers Diplomas , a Standard Foundation Program and Intensive Program , a UniReady Program , and two Masters Qualifying Programs
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engineering, and building machine learning models for tasks like classification and regression Key responsibilities will include: Research and Technical Support: Collaborate with researchers and HDR students