Dr David Bohan, Dr A Tamaddoni-Nezhad, Dr Alan Raybould
No more applications being accepted
Competition Funded PhD Project (European/UK Students Only)
About the Project
Developments in next-generation sequencing (NGS) of DNA, to identify species present in environmental samples, holds out the possibility that we could use these samples as ecological data. Machine learning might then be employed to reconstruct the networks of ecological interactions that structure ecosystem functions and services such as invasion, biological control and disease. In principle, if combined, NGS-derived ecological data and machine learning could allow us the exciting opportunity of biomonitoring and managing the earths ecosystems at high resolution (Bohan et al. 2017).
The machine learning methods that could be used to reconstruct networks of ecological interaction are in the very early days of development. Some methods have shown promise, but it is also clear that there are many problems yet to solve. Logic-based machine learning has already been used to successfully build insect food webs from classical ecological sampling data (Bohan et al. 2011; Tamaddoni-Nezhad et al. 2013). The aim of this PhD will be to develop logic-based methods to reconstruct a similar food web but from NGS DNA data as part of a French ANR project “NGB” (https://anr.fr/Projet-ANR-17-CE32-0011), which aims to develop methods for the next-generation of biomonitoring using network reconstruction.
Salary details
The monthly salary for the PhD is 1770€ (gross before tax).
Funding Notes
The funding for the PhD project is provided by the ANR NGB project and Syngenta.
The candidate will master methods necessary to reconstruct networks of ecological interaction. Thus, he or she will need academic knowledge and skills in the subject area of computer programming, data management and analysis (e.g. R). The ability to communicate in written and spoken English is obligatory. A background in Ecology (Community Ecology, Food Webs) will be viewed as highly beneficial. The candidate will also be motivated by interdisciplinarity and teamwork, as required by the ANR NGB project. Autonomy, organizational skills and methodological rigour are essential.
References
The PhD will be supervised by Dr David A. Bohan (INRA Bourgogne Franche Comté), Prof. Alan Raybould (Syngenta / University of Edinburgh) and Dr Alireza Tamaddoni-Nezhad (Imperial College / University of Surrey). The PhD will be based at INRA Bourgogne Franche Comté in Dijon, France. It is proposed that the PhD student spends up to 12 months of the three years of the PhD at the University of Surrey, UK, developing methods with Dr Tamaddoni-Nezhad.
To apply, send a cover letter (maximum one page), a detailed CV including in particular M1-M2's notes, and any details of contact people for recommendations to David A. Bohan (David.Bohan@inra.fr), Alan Raybould (alan.raybould@syngenta.com) or Alireza Tamaddoni-Nezhad (a.tamaddoni-nezhad@surrey.ac.uk) by the 20th June 2019. Those candidates that are selected as interesting will be interviewed at INRA Bourgogne Franche Comté.
Bohan, D.A. et al. (2011) Automated discovery of food webs from ecological data using logic-based machine learning. PloS One 6, e29028
Bohan DA, Vacher C, Tamaddoni-Nezhad A, Raybould A, Dumbrell AJ, Woodward G. (2017) Next-Generation Global Biomonitoring: Large-scale, Automated Reconstruction of Ecological Networks. Trends in Ecology & Evolution 32, 477–487.
Tamaddoni-Nezhad, A. et al. (2013) Construction and Validation of Food Webs Using Logic-Based Machine Learning and Text Mining. Adv Ecol Res 49, 225–289
Continue with Facebook
