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Field
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human disease, and developing and applying new computational algorithms to decipher it Enjoy working closely and collaboratively to solve complex biological problems Able to lucidly present complex
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. Key Responsibilities: Develop learning-based navigation algorithms for complex environments. Integrate perception and navigation modules into unmanned vehicle platforms using ROS or similar systems
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developing and devising machine learning algorithms in an energy context Ability to assess research resource requirements, and use resources effectively, to plan and manage research activity Strong analytical
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an opportunity for a Postdoctoral Fellow. You will contribute to UNSW’s research efforts in developing machine learning and deep learning algorithms for dynamic systems (sequential or time-series data). Experience
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designs and methods, clinical trial methods, Bayesian methods, and developing R packages and scalable algorithms. Opportunities for collaboration across the Department of Biostatistics and the Medical
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HiPerBreedSim project. In this role, you will leverage recent advances in working with ancestral recombination graphs (ARGs) to develop algorithms and code for simulating population genomic data, including
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or use existing simulation platforms to validate the developed algorithms and models. Analyse simulation data, and create visualizations to support research findings. Design and build prototypes
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to submit a professional development plan no later than one month after commencement of the postdoctoral period. It is expected that the successful candidate will be able to complete the project in
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real-time, and will be tested and demonstrated on a state-of-the-art HiL rig and an autonomous test vehicle. The post is focused on the development of automotive-grade algorithms and estimators that will
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amplifiers Develop scripts and algorithms for analog IC design Prepare report and conduct presentation at seminars Assist the school in various teaching activities, including instructing or supervision of labs