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Field
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opportunity to choose between two missions: • Mission 1: Improve new automated algorithmic schemes to quickly, efficiently and robustly detect and extract recorded geophysical signals related to earthquakes
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service quality. To address these challenges, advanced methodologies and algorithms are needed to design effective revenue and inventory management strategies for complex stochastic systems. The growing
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identification algorithms that directly interface with physical hardware. We work closely with industry partners, and our research has led to several methods now used in commercial products. We are part of
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navigation algorithms and machine learning models on physical robot platforms. We are particularly interested in candidates with expertise in generative AI and curriculum learning applied to robotics, as
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Toolkit customization, and performance tuning under hardware constraints; collaborating on robot motion planning, path optimization, and sensor data processing algorithms; implementing and testing ROS nodes
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to advanced control design and system optimization. Our specialty is developing embedded control, estimation, and identification algorithms that directly interface with physical hardware. We work closely with
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across time and contexts. Job Description: You will develop and apply mathematical models and machine learning algorithms to analyze the structure and evolution of knowledge systems across different
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conferences and journals. Overview: The successful candidate will join an interdisciplinary team focused on developing innovative numerical algorithms and software to address emerging challenges in scientific
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Edinburgh based team of Sandy Hetherington to lead efforts to quantitively characterise key morphological characteristics of fossil plants and assemble data on their ecology and global distribution
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a diverse team of biological and chemical scientists Experience with high performance computing environment (HPC) / cluster job submission Knowledge of statistical methods, data science algorithms