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Field
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postdoctoral candidate has an MD and/or PhD in neuroscience or related field and extensive experience with rodent neuroscience. Excellent analytical skills, e.g., Python & Matlab, are strongly preferred, as is
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. Your qualities You are curious and passionate about cellular agriculture, possess great problem-solving and analytical skills, and are highly motivated to contribute to the development of a safe and
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(LES). Strong knowledge in the analysis of large DNS and LES datasets, and the ability to use this data to develop reduced-order models for combustion simulations. Strong analytical skills and a proven
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organoid and across organoids, enhancing our theoretical understanding of the emerging information content within the single organoid and across the array, through the development of analytical and modelling
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desirable. Strong analytical capabilities and proficiency in the quantitative analysis of large datasets are also required. Click the 'Apply' button below to register an account with our recruitment system
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analytical models and systems and provides solutions and analyses to support strategic and tactical decisions. Adheres to University and unit-level policies and procedures and safeguards University assets
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. Analyze large datasets from battery systems to improve model accuracy and performance. Conduct research on predictive analytics for real-time battery health monitoring and fault detection. Collaborate with
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inorganic materials synthesis, heterogeneous catalysis and advanced analytical techniques including synchrotron radiation experiments. Applicants should also have experience in catalyst production systems
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work with rich datasets, advanced analytical tools, high-performance computing infrastructure, cutting-edge photonic sources, and modern industrial machinery. In this position, you will contribute
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, epidemiology, biostatistics, genomics, big data analytics, or artificial intelligence analytics Be familiar with conducting analysis with different types of data, including metagenomics sequencing, genomics