Now recruiting: Research Associate/Fellow in Antimicrobial Resistance Modelling

We are now recruiting the mathematical modelling post-doc for the EVAL-FARMS project. This post will work with me, Theo Kypraios in Maths, and the EVAL-FARMS team more generally, developing mathematical models for risk of emergence of AMR pathogens in agricultural waste, using all the exciting data that are being generated by the empirical researchers on the grant. Details of the advert, as well as links to it, are:

Research Associate/Fellow in Antimicrobial Resistance Modelling

Agricultural & Environmental Sciences

Location:  Sutton Bonington
Salary:  £26,052 to £38,183 per annum, depending on skills and experience (minimum £29301 with relevant PhD). Salary progression beyond this scale is subject to performance
Closing Date:  Wednesday 28 June 2017
Reference:  SCI158617

We are seeking an excellent researcher in modelling of antimicrobial resistance. The successful applicant will use mathematical and statistical models to make predictions on risk of emergence of antimicrobial resistant pathogens in a farm slurry system and slurry amended soil. The post is funded by NERC-led EVAL-FARMS project (Evaluating the Threat of Antimicrobial Resistance in Agricultural Manures and Slurries). Thus the role holder will work closely with an interdisciplinary team, including experimental researchers in microbiology and analytical chemistry, and social researchers in science and technology studies, in order to develop meaningful, data driven risk models that could inform policy and practise. The work will involve deterministic and stochastic models, Bayesian statistics, data analysis and presentation.

Applicants must have, or be very close to completing, a PhD in mathematical, computer or statistical models applied to a relevant area in the biological or environmental sciences. Research experience in applying such models in antimicrobial resistance, metagenomics, analytical chemistry and/or water quality would be desirable. Applicants must be able to demonstrate skills in Bayesian approaches, including relevant computational techniques such as MCMC, development and analysis of deterministic and stochastic models, programming in a relevant language (e.g. R, Python or Matlab) and a broader appreciation of science. Applicants must also be able to demonstrate research ambition through timely publication of research, coupled with commitment to the research project as part of their on-going career development. Excellent oral and written English language skills are essential.

The post is a joint appointment between the Schools of Biosciences and Mathematical Sciences. The post holder will normally work on the Sutton Bonington Campus, and will also have meetings on the University Park Campus with staff in the School of Mathematics and other collaborating schools.

Fixed term for 2 years from 1st September 2017

Applications can be made through the University of Nottingham web site. I am happy to receive informal enquiries.

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Welcome to Mike Stout

This week Mike Stout started work in our group as a research fellow on the BBSRC funded project to develop systems for high throughput analysis of cell growth data from BIOLOG phenotype arrays; a lay summary of this project can be found here.

Prior to this, Mike was a PDRA at the the Centre for Plant Integrative Biology, University of Nottingham, working with Professor Charlie Hodgman on developing repositories for multi-scale systems biology models and imaging data, and tools for systems biology simulation visualization. Mike’s PhD, also at the University of Nottingham, was on predicting geometric and topological properties of proteins using a range of machine learning systems, in particular Learning Classifier System. He has a background in both Biology and Computer Science and before his PhD headed the Electronic Journals Group at Oxford University Press, managing transnational projects to develop journal content online.

Mike’s research interests include Complex Systems Science, Evolutionary Computation, Functional Programming, Information Visualization and High Performance Computation using, for example, GPUs.

Mike’s experience and expertise will be particularly valuable for the group and we look forward to working with him.