OPEN POSITIONS
currently we don't have open positions
Description - You will work on an interdisciplinary research project at the School of Engineering, with tight collaborations from MRC Regenarative Medicine Center and Bayes Center at The University of Edinburgh. Your research will cover the theme of bio-impedance tomography, biosensor design, tissue engineering and data science.
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Enthusiastic and self-motivated candidates are sought with a background in sensing and imaging.
Funding - Tuition fees and stipend are available for Home/EU students (International students can apply, but the funding only covers the Home/EU fee rate). The applicants are also encouraged to apply for the University of Edinburgh Scholarships or China Scholarships Council/University of Edinburgh Scholarship.
Supervisors - Principal supervisor: Dr. Yunjie Yang
Assistant supervisor: Dr. Pierre Bagnaninchi (MRC Regenarative Medicine Center) and Dr. Jiabin Jia
Informal enquiries can be directed to y.yang@ed.ac.uk
PhD Position 2: Multi-modality on-chip sensing for 3-D cell culture monitoring. (Filled!)
Description - You will work on developing a multi-modality on-chip sensing/imaging techenique for non-destructive and real-time 3-D cell culture system monitoring. You will have the chance to work closely with our industrial collaborators and researchers in international research institutes.
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Enthusiastic and self-motivated candidates are sought with a background in electronics engineering and sensing and imaging.
Funding - The applicants are encouraged to apply for the University of Edinburgh Scholarships or China Scholarships Council/University of Edinburgh Scholarship. Self-funded applicants with a strong CV are also welcome to apply.
Supervisors - Principal supervisor: Dr. Yunjie Yang
Assistant supervisor: Dr. Pierre Bagnaninchi (MRC Regenarative Medicine Center)
Informal enquiries can be directed to y.yang@ed.ac.uk
Description -
Oil and gas occupies a significant sector in the world’s energy structure. The trend to construct digital oil fields based on internet of things has raised increasing demand for accurately monitor the health and production of the entire oil field down to individual wells in a real-time manner. Revolutionary flow measurement techniques have been developed to fulfil such requirements and the vision is to optimise reservoir management and production process. However, the explosive volumes of flow sensing/imaging data produced by these techniques has led to a pressing challenge. Given the significance of extracting information to the maximum, this project is to explore how can these data be effectively analysed and interpreted for optimising oil and gas production. This project is an emerging area of research with many uncertainties to be resolved. By making use of data science, effective connections between flow sensing/imaging data and process control, analysis and fault diagnosis will be identified. This will give guidance for the energy industry to improve efficiency, reduce cost and meet production goals.
Objectives of this PhD project include:
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Explore artificial intelligence based data analysis techniques, e.g. deep neural networks, to significantly improve the measurement accuracy of dynamic flow parameters for oil/gas/water flows.
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Develop a multi-scale, multi-dimensional and online oil and gas production monitoring, analysing and diagnosing data platform to aid decision making and production control.
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Perform pilot-scale and oil-field validations of the developed methods.
Successful applicant will work closely with the researchers from the Agile Tomography Group at the School of Engineering, the Bayes Centre and the School of Informatics at the University of Edinburgh, and Tsinghua University in China, to deliver high-quality interdisciplinary research and make impact on the oil and gas industry.
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Enthusiastic and self-motivated candidates are sought with a background in electronics engineering and sensing and imaging.
Funding - The applicants are encouraged to apply for the University of Edinburgh Scholarships or China Scholarships Council/University of Edinburgh Scholarship. Self-funded applicants with a strong CV are also welcome to apply.
Supervisors - Principal supervisor: Dr. Yunjie Yang
Assistant supervisor: Dr. Yang Cao (School of Informatics) and Dr. Yi Li (Tsinghua University)
Informal enquiries can be directed to y.yang@ed.ac.uk