71 evolution "https:" "https:" "https:" "https:" "https:" "https:" "ISCTE IUL" Postdoctoral research jobs at University of Oxford
Sort by
Refine Your Search
-
The postdoctoral researcher will lead the development of computational methods for aligning cortical organisation across species using transcriptomic and anatomical data combined with modern machine
-
-funded project, ML4MetaLigM. We are seeking a highly motivated Postdoctoral Research Associate to contribute to the development and deployment of MLIPs for supramolecular modelling, with a focus on self
-
The Mosher lab is searching for a postdoctoral research associate to lead a project visualizing small RNAs during seed development. Plants produce abundant siRNAs from a small number of loci during
-
About the role We are seeking a Postdoctoral Research Associate to support our projects to understand membrane evolution. The aim of this project is to understand the evolution of the membrane
-
. The successful candidate will contribute to this emerging field of AI-driven X-ray experimental science through the development of novel X-ray spectroscopic imaging experimental modalities, the design and
-
Neuroimaging, MRC BNDU, and the Oxford Health NIHR BRC. About You You will hold a PhD in biomedical engineering, neuroscience, or a related field, and have experience in the development of technological systems
-
on research pertinent to the project. By scaling up data, compute and model size, large language models (LLMs) have gained an impressive and ever growing array of capabilities. The next phase of development
-
, experimental development, and dissemination of results through presentations and publications. This is a fixed-term post for 12 months (in the first instance), working full-time for 37.5 hours per week, though
-
cavitation detection, imaging, and monitoring. You will be responsible for engaging in reactor design, construction, development, and characterisation. You will also be expected to integrate a cavitation
-
funding. You will be responsible for the development of novel acquisition, reconstruction, image analysis and/or modelling methods for cerebrovascular magnetic resonance imaging (MRI) to improve