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Field
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project establishes a scalable framework for automated atomic-scale image analysis that can be extended to more complex defect types, materials, and computational microscopy applications. Fellowship 2
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expected to self-direct research activities, prioritize tasks, and seek input as needed on complex scientific or methodological issues. Supervision Given The Postdoctoral Fellow will provide guidance and
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to work in a team Strong publication record or other evidence of scientific ability and maturity Excellent communication skills in English Experience in tackling complex scientific problems or method
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strong quantitative and programming expertise and a demonstrated interest in applying AI and machine learning approaches to complex biological systems. What We Offer -Access to clinical samples from well
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or more of the following areas: Controlled, living polymerization for synthesizing polymers with complex architectures (such as block, star, and dendritic polymers) and characterization of their microphase
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information on complex diseases. The goal of our efforts is to build and apply automated analytical pipelines for various types of pathology data, including histopathology images and multi-omics (e.g., genomics
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expression, trajectory inference, and pathway enrichment analyses. Integrate transcriptomic data across species, conditions, and experimental platforms. Interpret complex datasets in close collaboration with
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management and organisational skills in relation to research. Ability to communicate complex information clearly to a range of groups including public audiences Good IT skills (including MS Office, email
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PhD level qualifications. High level analytical capability Ability to communicate complex information clearly Experience of undertaking semi-structured interviews/focus groups and experience of data
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general aim is to develop new conceptual frameworks and models for the study of biological complexity, focusing on the intersection of development and evolution. The research group is primarily theoretical