Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- ;
- Cranfield University
- ; Swansea University
- University of Nottingham
- ; The University of Manchester
- ; University of Exeter
- ; University of Nottingham
- ; University of Southampton
- University of Cambridge
- ; University of Birmingham
- ; Brunel University London
- ; EPSRC Centre for Doctoral Training in Green Industrial Futures
- ; Newcastle University
- ; University of Oxford
- ; University of Plymouth
- ; University of Reading
- ; University of Warwick
- AALTO UNIVERSITY
- UNIVERSITY OF VIENNA
- University of Newcastle
- ; Cranfield University
- ; London School of Economics and Political Science
- ; Loughborough University
- ; Manchester Metropolitan University
- ; St George's, University of London
- ; The University of Edinburgh
- ; University of Bristol
- ; University of Cambridge
- ; University of Essex
- ; University of Stirling
- ; University of Surrey
- ; University of Sussex
- Abertay University
- Durham University
- Imperial College London
- University of Oxford
- University of Sheffield
- 27 more »
- « less
-
Field
-
This is an exciting opportunity to explore the role of complex microbial communities in promoting resilience and maximising yields in large scale algal bioreactors exposed to different environmental
-
, including: Genomic technologies – hands-on experience in long-read sequencing and variant interpretation Bioinformatics – pipeline development, visualisation, and statistical modelling PRS – applying big data
-
problems in health data science. Air pollution is composed of several different environmental pollutants, for example particulate matter (PM10 and PM2.5), ozone (O3), nitrogen dioxide (NO2) and sulphur
-
-impact questions in environmental economics and labour economics. Key responsibilities for this role includes: Data collection, cleaning, and merging from very large-scale microdata sources (e.g., terabyte
-
to the analysis of time series. In particular, the project will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show
-
will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show challenging properties of uncertainty, irregularity and
-
effective delivery of expertise, equipment, and medical resources in response to complex and large-scale emergencies across the United Kingdom. In its initial phase, the research will examine past and
-
, interdisciplinary research. Our department remains at the forefront of engineering innovation, delivering sustainable solutions with global impact. As a large, multi-specialisation department, we thrive
-
the analysis of the complex data and cellular models (Big Data and Kavli Institutes). The DPhil will provide the student with multidisciplinary skills including specialized training in bioinformatics, genetic
-
team of researchers using operational and information-theoretic tools to gain insights into quantum foundations, causality, and space-time physics. We are convinced that further progress on open problems