Sort by
Refine Your Search
-
at St. Stephen's Green/RCSI car park. Recognition: At RCSI, we value and recognise the contributions of our staff through various awards and events, such as Long Service recognition, the Vice Chancellor
-
Undertaking (IHI JU), SEARCH boasts an initial budget of over €15.2 million. SEARCH aims to develop high-quality synthetic datasets that simulate real healthcare data for research, AI, and machine learning
-
experience Evidence of experience with public-patient involvement and stakeholder engagement Track record in scientific publication and dissemination Experience with Machine Learning Algorithms and Artificial
-
): Research experience with literature review, data collection from different stakeholder groups. Advanced skills in using Stata or R. Machine Learning experience Artificial Intelligence Excellent written, oral
-
as world-leading or internationally excellent. We use this expertise to teach the next generation of health professionals and research scientists. Based across King’s Denmark Hill, Guy’s, St Thomas
-
at St. Stephen's Green/RCSI car park. Recognition: At RCSI, we value and recognise the contributions of our staff through various awards and events, such as Long Service recognition, the Vice Chancellor
-
(EHR), health information exchanges, and data analysis software. Experience with health IT innovation, including working with artificial intelligence, machine learning, telemedicine, or mobile health
-
in advanced signal processing techniques and good understanding of emerging machine learning methodologies used in NDE. You will work in close collaboration with project partners at the University
-
at St. Stephen's Green/RCSI car park. Recognition: At RCSI, we value and recognise the contributions of our staff through various awards and events, such as Long Service recognition, the Vice Chancellor
-
neurocritical care research The Opportunity We are seeking a Research Fellow - Data Science professional with strong expertise in machine learning, deep learning and high-frequency physiological signal analysis