Sort by
Refine Your Search
-
and industry coordination. The postdoc will contribute to empirical research, scholarly publications, and the design of human-AI collaboration models. Work will be based at the University of Nottingham
-
environment embedded within the Centre of Metabolism, Ageing and Physiology (CoMAP), based at University of Nottingham School of Medicine, Royal Derby Hospital. You will work alongside basic scientists and
-
be working in a multidisciplinary environment embedded within the Centre of Metabolism, Ageing and Physiology (CoMAP), based at University of Nottingham School of Medicine, Royal Derby Hospital. You
-
Foundation at the interface of cancer biology and neuronal function investigating how cancer and cancer chemotherapy impacts on sensitisation and pain. As part of a multidisciplinary team based in Nottingham
-
joint multi-state models for health care processes and disease progression”. The central aim of this 36 month project is to develop new methods for the joint modelling of disease or other healthcare
-
on sensitisation and pain. As part of a multidisciplinary team based in Nottingham, this Post-Doctoral Research Associate/Fellow will support the project entitled “Extracellular vesicles as conduits for the transfer
-
illuminate data, interprets reports, evaluate and criticise texts and bring new insights. Ability to creatively apply relevant research approaches, models, techniques and methods. Ability to assess and
-
. Quantitative Analysis: Demonstrated ability to handle multimodal datasets, conduct statistical analysis, and apply predictive modeling and validation techniques. Research & Publication: Skilled at critically
-
will join a dynamic, interdisciplinary team exploring the frontiers of mass spectrometry imaging and molecular modelling. As our Research Fellow, you will lead the computational arm of a Leverhulme Trust
-
as to be able to feed into the recently elected government's policy discussions. The main form of analytic techniques used will be time series modelling, age-period-cohort modelling and structural