42 data-"https:"-"https:"-"https:"-"https:"-"INESC-ID" research jobs at King's College London
Sort by
Refine Your Search
-
Listed
-
Country
-
Program
-
Field
-
conditions. About the role We are seeking a highly motivated postdoctoral Research Associate in research data science and analysis to join Professor Gerome Breen’s internationally recognised team at King’s
-
. The PDRA will contribute to the development of a database of high-resolution equity indicators and apply cutting-edge GeoAI and spatial data science techniques to model and classify population health
-
Development for more information. About you To be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria Fluency in English Strong skills in
-
to ensure data collection and analysis is conducted within timelines. The successful applicant must be able to work independently and collaboratively within a diverse broader research team. The project will
-
well as interpreting multi-omics datasets. The role will therefore require state-of-the-art skills and confidence in wet-lab research, data analytics, and oral and visual communication. This is a full time 35 hours per
-
clinical study. The post holder will hold a doctoral degree or equivalent in an addiction related topic and need to have expertise in working with data from the National Drug Treatment Monitoring System
-
surveillance, and will contribute to the development of novel approaches for managing confidential spatial health data, including differential privacy and other secure geospatial data protocols. This research
-
related topic and need to have expertise in working with data from the National Drug Treatment Monitoring System (NDTMS), working with government and expertise in quantitative prediction modelling
-
of Biomedical Engineering & Imaging Sciences. About The Role The research associate will lead the development of cutting-edge multi-modal MRI foundation models. These models will leverage both imaging data and
-
responsibility is to test key assumptions about differential disease risk by integrating high-resolution socio-ecological, environmental, and novel health data from individual and population sources. This research