PhD Studentship
School of Computer Science
University of Nottingham -ICOS Research Group
Mining the gene networks involved in water-stress
response in bambara groundnut: a machine learning approach to
translating traits in model species to minor crops.
Underutilised species have the potential to help diversify agriculture and to make food production more robust in the face of future challenges. The ability to use information obtained in one species to guide evaluation of another species is the underlying principle behind the use of model systems.
The translation of data from models and major crop species to underutilised species is essential if underutilised species are to make a major contribution to Global Food Security or other end-uses.
The ability to identify key components of trait networks relies on developing methods to search very large datasets, guided by knowledge from other systems. Machine learning techniques have been shown to be very competent at this task, as they are able to explore vast datasets to identify key genes and their interactions. Moreover, they can easily integrate multiple sources of knowledge and automatically identify the relevant parts of the system being analysed.
This project will focus on the use of machine learning and bioinformatics techniques to study the process of water-related stress in bambara groundnut (an African legume) by contrasting datasets from this species with the known networks of model organisms. This legume grows successfully with varying access to water, cropping from the Namib desert in Africa to the humid tropics in Indonesia.
The student will spend time at the UK and Malaysia campuses of the University of Nottingham. In the UK, the student will be based at the Interdisciplinary Computing and Complex Systems (ICOS) research group(http://icos.cs.nott.ac.uk) at the School of Computer Science. In Malaysia, the student will be co-hosted by Biosciences at UNMC: www.nottingham.edu.my/biosciences/index.aspx and the new Crops for the Future Research Centre currently under construction.
The student will be registered formally at The University of Nottingham Malaysia Campus. The studentship provides full tuition fees and a monthly stipend of 4400MYR (Malaysian Rynggit), for a duration of 3 years.
Supervisor names and contact details :
Festo Massawe, School of Biosciences, UNMC (festo.massawe@nottingham.edu.my)
Jaume Bacardit, School of Computer Science, UoN(jaume.bacardit@nottingham.ac.uk)
Sean Mayes, Plant and Crop Sciences, UoN (sean.mayes@nottingham.ac.uk))
Michael Holdsworth, Plant and Crop Sciences, UoN(michael.holdsworth@nottingham.ac.uk) )
Students must have 2.1 or 1 class honours degree, or a combination of qualifications and/or experience equivalent to that level and have training in data mining techniques or bioinformatics. Previous biology experience will be an advantage for Computer Science applicants. The successful candidate should be confident working within and across a range of activities, have excellent organisational abilities, be enthusiastic and have good interpersonal skills.
To apply, students should send cv and all relevant documentation to Dr Jaume Bacardit, via the apply button below. Please quote ref. SCI/1254.
This studentship will remain open until a suitable student is found, thus we recommend applying early.
Underutilised species have the potential to help diversify agriculture and to make food production more robust in the face of future challenges. The ability to use information obtained in one species to guide evaluation of another species is the underlying principle behind the use of model systems.
The translation of data from models and major crop species to underutilised species is essential if underutilised species are to make a major contribution to Global Food Security or other end-uses.
The ability to identify key components of trait networks relies on developing methods to search very large datasets, guided by knowledge from other systems. Machine learning techniques have been shown to be very competent at this task, as they are able to explore vast datasets to identify key genes and their interactions. Moreover, they can easily integrate multiple sources of knowledge and automatically identify the relevant parts of the system being analysed.
This project will focus on the use of machine learning and bioinformatics techniques to study the process of water-related stress in bambara groundnut (an African legume) by contrasting datasets from this species with the known networks of model organisms. This legume grows successfully with varying access to water, cropping from the Namib desert in Africa to the humid tropics in Indonesia.
The student will spend time at the UK and Malaysia campuses of the University of Nottingham. In the UK, the student will be based at the Interdisciplinary Computing and Complex Systems (ICOS) research group(http://icos.cs.nott.ac.uk) at the School of Computer Science. In Malaysia, the student will be co-hosted by Biosciences at UNMC: www.nottingham.edu.my/biosciences/index.aspx and the new Crops for the Future Research Centre currently under construction.
The student will be registered formally at The University of Nottingham Malaysia Campus. The studentship provides full tuition fees and a monthly stipend of 4400MYR (Malaysian Rynggit), for a duration of 3 years.
Supervisor names and contact details :
Festo Massawe, School of Biosciences, UNMC (festo.massawe@nottingham.edu.my)
Jaume Bacardit, School of Computer Science, UoN(jaume.bacardit@nottingham.ac.uk)
Sean Mayes, Plant and Crop Sciences, UoN (sean.mayes@nottingham.ac.uk))
Michael Holdsworth, Plant and Crop Sciences, UoN(michael.holdsworth@nottingham.ac.uk) )
Students must have 2.1 or 1 class honours degree, or a combination of qualifications and/or experience equivalent to that level and have training in data mining techniques or bioinformatics. Previous biology experience will be an advantage for Computer Science applicants. The successful candidate should be confident working within and across a range of activities, have excellent organisational abilities, be enthusiastic and have good interpersonal skills.
To apply, students should send cv and all relevant documentation to Dr Jaume Bacardit, via the apply button below. Please quote ref. SCI/1254.
This studentship will remain open until a suitable student is found, thus we recommend applying early.
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