-
of London. This Welcome Trust Funded post will be based at the Centre for Molecular Cell Biology (CMCB) within the School of Biological and Behavioural Sciences (SBBS) at Queen Mary. The project is focused
-
-edge machine learning techniques will be used, including Large Language Models (LLMs). About Queen Mary At Queen Mary University of London, we believe that a diversity of ideas helps us achieve the
-
and/or evolutionary biology, with significant experience in embryological methods, single-cell/nuclei approaches, and general molecular biology techniques. A track record of high-quality published
-
potential applications in audio and music processing. Standard neural network training practices largely follow an open-loop paradigm, where the evolving state of the model typically does not influence
-
et al, Leukemia 2018; Poynton et al, Blood Adv 2023; Coulter et al, J Mol Diagn 2024). The wet lab/computational biology postdoc will lead a project investigating residual follicular lymphoma cell
-
project investigating mechanosensing in Diptera. This post will focus on using detailed wing geometry models and kinematic measurements in computational fluid and structural dynamics simulations to recover
-
responsibilities will include: Pre-registering data analysis plans; Leading and conducting advanced statistical analyses (e.g., twin/family designs, genomic and epidemiological methods, longitudinal modelling
-
infrastructure enables recruitment of 200-300 severely injured patients annually as part of the ACIT study. We also have a well-established experimental modelling group with full ethical approvals in place for all
-
responsibility for implementing a deep learning work-package as part of a Cancer Research UK-funded programme, developing an image-recognition model to identify morphological features corresponding to clonal