30 phd-computer-science-fully-funded Fellowship research jobs at University of London
Sort by
Refine Your Search
-
for a Research Fellow in Bioinformatics/Computational Biology to help develop, coordinate, and conduct robust analysis of high-throughput host protein data under supervision using advanced analytical and
-
Holloway, University of London. This position is funded by an ESRC research grant awarded to Professor Kathy Rastle in collaboration with Professor Denis Drieghe (University of Southampton) and Dr Sami
-
researchers from a range of disciplines, contributing insights and approaches from psychological science and related areas into the lab’s ground-breaking collaborative R&D programmes. As Research Fellow you
-
postgraduate degree, ideally a PhD, in statistics, machine learning, or a related field. Experience of developing new statistical methods and a strong working knowledge of a statistical software package, such as
-
to work flexibly across campuses and teams along with good computing skills. A higher degree would be desirable. About the School/Department/Institute/Project The Barts Cancer Institute (BCI) is a Cancer
-
independently and in close collaboration with in-country partners. The applicant should have an excellent academic track record that includes formal training in microbiology as well as a relevant PhD (public
-
Science and Services. You will be expected to enhance the department’s reputation through scholarship in clinical activities and teaching. You will achieve this by delivering professional services within
-
in partnership to achieve excellence in public and global health research, education and translation of knowledge into policy and practice. The Baby Ubuntu programme is a group-participatory programme
-
empirical research. They will oversee specific research tasks, develop new techniques, and generate original contributions to the programme while fostering a collaborative team environment. Key
-
degree, ideally a PhD, in health economics, medical statistics, data science, epidemiology or a related field. A clear conceptual understanding of causal inference methods such as instrumental variable