41 genetic-algorithm-computer "Integreat Norwegian Centre for Knowledge driven Machine Learning" Fellowship positions at The University of Queensland in Australia
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research Facility located at Warwick QLD. Key responsibilities will include: Research: Establish a research program, collaborate on research projects, seek and manage research funding, publish in reputable
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You For Level A Applicants will have completed or be near completion of a PhD in quantitative genetics, animal breeding, computational biology, or a related field. Additionally, you will demonstrate
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Quantitative Genetics, Statistical Genomics, Computational Biology, Plant Breeding, or a related field. Strong expertise in GWAS and post-GWAS analysis (e.g., fine mapping, gene network modelling, GWAS boosting
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programming and data analysis. Interest in developing methods, algorithms or software. Evidence of publications in high-quality peer-reviewed journals. Excellent communication skills. Experience
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classical-quantum algorithms for strongly correlated materials. Key responsibilities include: Research: Establish a research program, collaborate on research projects, seek and manage research funding
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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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dynamic research environment. Key responsibilities will include: Research: Establish a research program, collaborate on research projects, seek and manage research funding, publish in reputable journals
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(Academic Level A) Visa sponsorship may be available for this position Based at our St Lucia Campus - Brisbane About This Opportunity If you are passionate about computational modelling, thrive on solving
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research outputs in high quality outlets, participate in research funding applications, collaborate on joint research projects, contribute to transfer of knowledge, develop a research program, and utilise
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on clusters and high-performance computing infrastructure), Information Retrieval methods, Machine Learning algorithms, wrangling large-scale datasets, and showcasing the research results. The ability to work