Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
and esteemed industrial and governmental partners. Your expertise in machine learning, statistical analysis and programming will drive impactful research aimed at solving real-world challenges. Why join
-
. This data will help shape paediatric-specific T2T endpoints and facilitate the development of personalized treatment strategies. You will apply advanced statistical and machine learning methods, and your
-
analysis methods; risk prediction models/ machine learning/ causal inference methods/ signal and data processing and optimisation. You will be enthusiastic and committed to working in a field of health
-
An opportunity to lead work on materials design and discovery for an exceptional researcher with experience in developing and applying Artificial Intelligence and Machine Learning tools to materials
-
both machine learning and symbolic AI. The discovery of new solid electrolytes is a core project target. You will have a PhD in Chemistry, Physics or Materials Science. The post is available from 1 May
-
¿. This team is developing a new approach to materials design and discovery that combines experiment with computation, exploiting both machine learning and symbolic AI. Experimental materials synthesis is the
-
machine learning for functional materials design. You should have a PhD in Computer Science, Chemistry, Physics, Materials Science, or a related discipline, with a commitment to developing collaborative
-
the development of machine learning and AI software solutions for the prediction of atrial fibrillation in patients after stroke. There will opportunities for involvement in other health data research projects
-
structure prediction and machine learning) and is funded through the Leverhulme Research Centre for Functional Materials Design. For example, we work closely with experts in computer science, and the
-
to teaching (including development and delivery of CPD training), and research (bringing causal inference and machine learning/AI techniques) to mobilise data into action on prevention and early diagnosis