Sort by
Refine Your Search
-
myocytes or iPSC-derived cardiomyocytes. The position is based in Dr Joseph Burgoyne’s group at the Rayne Institute, St Thomas’ Hospital, part of King’s College London. The successful candidate will
-
(preferably neuroimaging) Computationally efficient deep learning Deep learning model generalisation techniques. Translating deep learning models to the clinic The post holder will be based in the Department
-
of the microbiome in olfaction using mouse models. The selected applicant will join the vibrant and friendly Tucker lab and work as part of a team interacting with the group of Prof Mike Curtis. The postdoc will
-
of liver micrometastases development in cancer, based on a novel MRI approach which combines multi-dimensional diffusion-relaxometry acquisitions, efficient data denoising and biophysical modelling
-
better position. A model is needed into which to feed critical information and retrieve cause/effect insights on which to base logical decisions. Biological information cuts across diagnostic boundaries
-
cardiovascular disease progression and outcomes. The successful candidate will work to identify key clinical and immunological predictors, develop risk models, and help generate new hypotheses to inform future
-
Guy’s Campus, Denmark Hill and UCL. About the role: To investigate the cellular mechanisms underlying schizophrenia-related symptoms in animal models (mice), in the context of a collaborative project with
-
of Biomedical Engineering and Imaging Sciences is a cutting-edge research and teaching School dedicated to development, translation and clinical application within medical imaging and computational modelling
-
(e.g. nanopore-based sequencing, reverse genetics, immunoblotting, luciferase-based assays, RT-qPCR, microscopy) to study virus evolution and innate/adaptive immunity in the context of viral infection
-
Science, Robotics, AI, or a related field 2. Strong background in machine learning and robotics, with specialisation in one or more of the following areas: generative models, reinforcement learning, human