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significant mathematical and computational background to learn and develop new AI methods. While demonstrated experience in computer vision and deep learning for pathology or other biomedical image analysis is
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[map ] Subject Areas: Numerical Analysis/Scientific Computing Appl Deadline: none (offers accepted posted 2025/04/18) Position Description: Apply Position Description The Department of Mathematical
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-temporal analysis, use of latent variable models such as VAEs, GANs, and diffusion models to capture complex distributions, methods for interpretability (e.g., SHAP values), as well as uncertainty
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estimation (7) Mathematical analysis and modeling of social behaviors where numbers (1)-(7) above correspond to the same numbers in the [Laboratory] section above. Applicants will be considered
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to the development of Ecological Risk Analysis Tables (ERATs) to support the development of coastal management accounting for climate change and species interactions. The post-doc will have an important role in
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methods), Gaussian Random Fields, mathematical foundations of deep learning, functional analysis and measure theory. Research competence & initiative demonstrated through international publications in peer
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to help monitor and combat online social influence and promote a healthy information environment. The successful candidate will join the School of Computing and Mathematical Sciences as part of a project
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analysis of PDEs (with deterministic and/or stochastic methods), Gaussian Random Fields, mathematical foundations of deep learning, functional analysis and measure theory. You can find more information about
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Applications should include: Curriculum Vitae, including a description of the mathematical background of the candidate Cover letter Copies of relevant certificates Names, positions and addresses
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Leedham (colorectal cancer biology), Dan Woodcock (cancer genomics), Helen Byrne (mathematical modelling), and Jens Rittscher (computational pathology and imaging AI), offering a unique opportunity to work