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Field
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, fairness, and data ethics. Our research is rooted in basic research and centres on mathematical models of the physical and virtual world, as a basis for the analysis, design, and implementation of complex
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techniques including deep neural networks and large language models (such as GPTs), with state-of-the-art functional genomics approaches, including crop genomics, genome editing, and single cell multiomics
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Do you have experience with modelling structures subjected to dynamic loading? Are you interested in data-driven methods for modelling applied loading? Are you eager to share your knowledge within
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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with
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, Physics, Geosciences or a closely related field Experience with mathematical inverse methods, global geo-/paleomagnetic field modelling expertise is an advantage Knowledge of geomagnetic field evolution
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work on large-scale understanding of coastal wetlands - primarily mangrove forests and tidal marshes. This will include mapping and modelling of distribution, value, condition, and opportunities
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, Applied Mathematics, Statistics or related computational field. Superb quantitative background, strong coding skills (e.g., Python, R). Expertise in infectious disease modeling. Strong record of peer
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work on large-scale understanding of coastal wetlands - primarily mangrove forests and tidal marshes. This will include mapping and modelling of distribution, value, condition, and opportunities
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visit our website at: https://rede.ecu.edu/postdoctoral/ Minimum Education/Experience: Excellent written and oral communication skills PhD in Physics, Biophysics, Biochemistry, or a related field Prior
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Criteria: Qualifications A good first degree in Physics or Mathematics. A PhD (or be close to submission) in Theoretical or Mathematical Physics or a related subject. Experience Experience in conducting high