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
-
data and finite element modelling is a plus. You will have experience in writing publications, and be happy working in a collaborative environment. About the School The role will be based at the School
-
platforms at our prestigious Centre for in vitro Predictive Models (https://www.cpm.qmul.ac.uk/ ), and work with project partners based at the Cross Institute Advanced Tissue Engineering (CREATE) lab
-
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
-
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
-
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
-
including cell culture, organ-chip models, tissue engineering, and musculoskeletal biology. The PDRA will plan and conduct experiments, generate high-quality data, prepare publications, make presentations and
-
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
-
developed human discovery 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