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on a flat-belt conveyor and are dynamically grasped, unfolded, and oriented by multi-axis manipulators fitted with custom force-feedback grippers, and will work in close concert with a parallel sensing
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. The Software Engineer will be responsible for maintaining high standards around code quality via improving design, pair programming, code reviews, etc. The code should take advantage of parallel processing to run
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data resources for different research applications Knowledge of computer programming languages Experience of working in a high-performance computing environment (e.g. Slurm) and/or with public/private
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of, the different laboratories working both collaboratively and creatively with staff and administrative leadership to address complex technical, facility, and HR related issues to improve processes and integrate
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of laminar/neuropixel probes and electrical microstimulation to study attention and decision making networks in a behaving animal model together with parallel studies in humans. The project is part of a NIMH
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driven science/tools. Teaching or training experience. Experience using large-scale HPC clusters. Experience supporting research computing environments. Familiarity with parallel programming models such as
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strengthening research capabilities in the region. The candidate will be part of the WAARC Postgraduate Cohort and supported through WAARC’s Postgraduate Research Scholarship Program. The candidate will work
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: Rethinking AI for Just and Sustainable Futures programme. CFI is a highly interdisciplinary research centre addressing the challenges and opportunities posed by artificial intelligence (AI). Funded by
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: Rethinking AI for Just and Sustainable Futures programme. CFI is a highly interdisciplinary research centre addressing the challenges and opportunities posed by artificial intelligence (AI). Funded by
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2022) and 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