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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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algorithms might support the wider integration of, and uptake of, renewable energy technologies for particular use cases and considering a variety of perspectives (technical/policy/social/economic). You will
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of classical and hybrid classical-quantum algorithms for treating the correlations. This position offers exciting opportunities for collaboration within UQ, across the QDA network, and with external research
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Condensed Matter Physics and Materials Sciences o Theoretical and Computational Biophysics o Soft Matter Physics o Physical Chemistry and Theoretical Chemistry o Combinatorics, Algorithm, Extremal Graph
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analytical techniques, with a focus on quantifying energy output and sensitivity to initiation by external stimuli. The project will be jointly supervised by Prof. Colin Pulham in the School of Chemistry at
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, Algorithm, Extremal Graph Theory, Computing Theory o Programming Language, AI Theory or Machine Learning o Classical and Quantum Algorithm for Computational Quantum Many-body Theory o Theory and Computation
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the Neurogenesis and Brain Cancer group led by Prof. Simona Parrinello. The team uses a variety of state-of-the-art technologies, including advanced imaging, single cell and spatial ‘omics’, barcoding, mass
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applicant's accessibility needs. If you require accommodation during the interview process, please contact Prof. Nahee Park at nahee.park@queensu.ca. Application Materials Required: Further Info: https
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-informed machine learning (PIML) models for the prediction of physical and chemical properties using data from experiments and computation constrained by physics requirements. § Implementing algorithms
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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