Sort by
Refine Your Search
-
About the Role A Postdoctoral Research Assistant (PDRA) is sought to join the musculoskeletal organ-chip research team within Queen Mary’s Centre for Predictive in vitro Models. This 23-month post
-
project is to develop a series of surrogate models focusing notably on Physics-Informed Neural Networks to emulate the process of sediment deposition, diagenesis, and potentially fracturing, working closely
-
About the Role The combination of personalised biophysical models and deep learning techniques with a digital twin approach has the potential to generate new treatments for cardiac diseases. Our
-
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
-
In Vitro Predictive Models to Explore Tendinopathy”. The project is funded by the Medical Research Council (MRC) and part of the organ-chip research work underway within the Centre for Predictive in
-
. Applicants should have a PhD in Cultural Geography, Environmental Arts or a closely related field; knowledge of current climate- and ocean-related scholarship in the Blue Humanities; a track record of
-
to work on a project investigating mechanosensing in flies (Diptera). This post will focus on using detailed wing geometry models and free flight kinematic measurements in computational fluid and structural
-
migration and in vivo murine models of inflammation and technical expertise in complex flow cytometry is essential. The previous ability to work with rodents would be advantageous, whilst experience in
-
disease progression. About You Applicants should hold a PhD degree or equivalent in biological or related science and have a strong background in immune cell biology and animal models of inflammatory and/or
-
2025. We seek to recruit a Research Associate specialising in statistical modelling and machine learning to join our multi-university multi-disciplinary team developing a groundbreaking technique based