50 data-"https:" "https:" "https:" "https:" "https:" "https:" "SciLifeLab" Fellowship research jobs at University of Nottingham
Sort by
Refine Your Search
-
rewards, including fitness and health facilities, staff discounts, travel schemes and many more. To find out more about what we can offer you, follow the link to our benefits website Further information is
-
their data. This will require effective communication with researchers from diverse backgrounds and the ability to translate between biological questions and computational analysis. You will be expected
-
identify barriers to early diagnosis. To do this we will use a large United Kingdom general practice dataset, which is linked to cancer records. This contains information on almost 4000 people who have never
-
focuses on developing cutting-edge statistical/machine learning methods for fitting complex, multi-institutional network models to partially observed hospital infection data. This research will directly
-
strategies. Experience with qualitative research collection and analysis techniques, including at least one of the following: conducting data collection interviews, workshops, and/or textual analysis, is
-
each year. Please check role profile for further information. For informal inquiries, please contact David Bates - david.bates@nottingham.ac.uk . Please note that applications sent directly to this e
-
and contribute to the achievement of specific research objectives. For more information, please refer to the role profile. Requests for secondment from internal candidates may be considered on the basis
-
theoretical areas including statistical physics, condensed matter theory, computational physics, quantum information, and machine learning. We seek motivated, skilled and highly independent researchers
-
infectious aerosols in buildings (ASHRAE Standard 241). The work involves analysis, simulation, and integration of real-world data into risk models that directly inform how buildings are designed and operated
-
searches and meta-analyses of dose–response and exposure–response data to support model calibration and uncertainty analysis. This role offers a rare opportunity to bridge public health and building science