Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
, including meta-analysis using PLINK 1.9 (fixed-effects inverse-variance model), conditional analysis using REGENIE, and fine-mapping of HLA–protein associations. You must hold a first degree in Genomic
-
have completed, or be close to completing, a PhD/DPhil in a relevant quantitative field such as computational social science, computer science, or cognitive science. They will have a demonstrable track
-
annum inclusive of Oxford University weighting Potential to under fill at grade 06RS: £34,982-£40,855 per annum inclusive of Oxford University weighting The Department of Computer Science seeks to employ
-
intelligent, and politically astute Proven leadership and team management skills Experienced in full programme lifecycle management Strong analytical and financial modelling skills Proficient with digital tools
-
(LiB’s). You will be responsible for: • Developing models and simulations of the electrode fabrication process, sensors, and actuators. • Developing a demonstrator of a soft sensing system that
-
. Concurrently, you will develop lower order analytical models and perform high fidelity computational simulations to corroborate experimental findings and propose other configurations to be subsequently
-
). The post is funded by NIHR and is fixed-term for 24 months, with a possible extension. This project is about creating novel AI models to predict patient outcomes following acceptance or refusal of an offer
-
computational sciences, decision-maker education campaigns, and training the next generations of technology governance leaders. It is one of the few organisations in the world to focus on the governance of AI
-
challenge. We seek a senior computational biologist to apply these extensive in-house datasets toward the development of novel, domain-tailored machine-learning models and analytical methods. You will explore
-
and realign how we measure and model populations by infusing new types of data, methods and unconventional approaches to tackle the most challenging demographic problems of our time. We are looking