Sort by
Refine Your Search
-
Listed
-
Employer
- University of Oxford
- ;
- KINGS COLLEGE LONDON
- Durham University
- University of Cambridge
- University of London
- AALTO UNIVERSITY
- King's College London
- DURHAM UNIVERSITY
- Heriot Watt University
- UNIVERSITY OF VIENNA
- University of Liverpool
- Nature Careers
- University of Birmingham
- ; University of Cambridge
- ; University of Oxford
- Imperial College London
- ; Technical University of Denmark
- ; University of Copenhagen
- ; University of Kent
- Cardiff University
- Medical Research Council
- Swansea University
- University of Manchester
- ; King's College London
- ; Royal Holloway, University of London
- ; The Francis Crick Institute
- ; University of Dundee
- ; University of Exeter
- Aston University
- Birmingham City University
- Manchester Metropolitan University
- Royal College of Art
- Sheffield Hallam University
- University of Glasgow
- University of Hull
- University of Leicester
- University of Lincoln
- University of Newcastle
- University of Reading
- University of West London
- 31 more »
- « less
-
Field
-
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
-
help develop and characterise advanced patient-derived tumour models and use them to test promising therapeutic targets that exploit vulnerabilities caused by loss of the SMARCB1 gene. This role offers
-
, penetration and toxicity of novel metallodrug-coated gold nanoparticles in a 3D-cell culture model of head and neck cancer. This is a highly inter-disciplinary project involving collaboration between Dr Nik
-
aims at addressing computational challenges associated with data acquisition and information extraction from complex sensors and sensor networks. Crucially, uncertainty management and quantification
-
organoid and across organoids, enhancing our theoretical understanding of the emerging information content within the single organoid and across the array, through the development of analytical and modelling
-
semiconductors for renewable energy generation”. This collaborative project will tackle the complex array of exciting fundamental science arising in “soft” inorganic and hybrid semiconductors, seeking to develop
-
are comfortable navigating complex HPC environments and wrangling large datasets. You have experience with modelling through state-of-the-art machine and deep-learning methods and with hands
-
expertise in organoid models of neurodevelopment to join our team and be involved in a MRC funded project aimed at generating region-specific brain organoids and assembloids from multiple gene-edited human
-
complex information clearly Fluency in relevant models, techniques or methods and ability to contribute to developing new ones Ability to assess resource requirements and use resources effectively
-
(PISCES) with different climate change scenarios and biogeochemical model complexities over centennial and millennial timescales. This will allow you to identify the key drivers of biological carbon storage