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interaction, electronic health record data, or large-scale biobank resources. Experience with high-performance computing, reproducible workflow development, scientific manuscript preparation, or collaborative
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describing manufacturing process and structural performance are of nonlinear and multiscale nature. As those high fidelity models are not suitable for control applications, equivalent and efficient physics
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areas of environmental change, remote sensing, infectious disease surveillance, spatial epidemiology, machine learning and global health. Key responsibilities: - Conduct high quality research in
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. On the other hand, the physics-based models describing manufacturing process and structural performance are of nonlinear and multiscale nature. As those high fidelity models are not suitable for control
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. Applicants with complementary backgrounds and a strong interest in expanding their expertise in insect ecology, microbiome analyses, shotgun meta-genomics, microbial functional annotation, and high-performance
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with experience assisting with data management and analysis, including computational and statistical analysis. Sound organisational skills and a high level of attention to detail, including the ability to progress
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(meta-)genomes Experience in the computer-assisted analysis of large biological datasets (e.g., using R, Python, and Bash/Linux environments) Very good written and spoken English skills Ability to work
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particular emphasis on LiDAR-Inertial Odometry (LIO), Visual-Inertial Odometry (VIO), and multi-sensor fusion for UAVs and other agile platforms. The Research Fellow will develop high-performance, robust
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one of the following fields: process-based model development, Earth system model simulation and analyses, data assimilation, large-scale data collection and synthesis, high performance computing, and
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on delivering high-quality teaching and diverse, real-world research. We specialise in biosciences, chemistry, computing and technology, as well as engineering, forensic science, mathematics, physics