Fitting Models to Data – Promotional Video

The promotional video from the Wellcome Trust Parameterization workshop led by Joanne Dunster and Sara Jabbari has been posted on youtube:

The workshop was really great fun – as well as useful and informative – mainly due to the fantastic team  (Joanne, Sara, Theodore Kypraios and Simon Preston) – and I met lots of really great people.

Definitely one to run again!

Research Technician (2 Posts – 1 Full-time & 1 Part-time)

We are now recruiting for two technician positions for the EVAL-FARMS project.

Closing Date
Friday, 28th October 2016
Job Type
Technical Services
School of Biosciences – Technical Services
£22249 to £26537 per annum (pro rata if applicable), depending on skills and experience. Salary progression beyond this scale is subject to performance

Applications are invited for the above full-time and part-time posts which are based within the School of Biosciences at the Sutton Bonington Campus.

The post is to provide technical support on a NERC funded research project “Evaluating the Threat of Antimicrobial Resistance in Agricultural Manures and Slurries”.

The role holder will assist with the collection of soil & slurry samples & processing the samples for microbiological, genomic, wet chemistry & water quality indicators and will require working off-site.

Duties will include:

  • Processing samples for further analysis by LC-MS, ICP/AAS, PCR and microbiological analysis and culture,general microbiological analysis & culture at ACDP 2,assessing water quality indicators using UV vis spectrophotometer
  • Ensuring stocks & equipment in own areas of responsibility are maintained & available for use.
  • Maintaining a safe working environment in accordance with statutory & University Health & Safety procedures.

Full details can be found in the job description.

Candidates must have a HNC in a relevant subject or equivalent qualifications plus considerable relevant technical/scientific experience OR substantial work experience in a relevant technical or scientific role.

Candidates should have experience of working with ACDP 2 pathogens and proven technical and/or experimental expertise in techniques for water quality analysis including filtration, COD analysis, molecular biology & PCR technologies.

These posts are available as soon as possible on a fixed-term contract for a period of 15 months.

Informal enquiries may be addressed to: Dov Stekel tel: 0115 9516294 Or email Please note that applications sent directly to this email address will not be accepted.

The University of Nottingham is an equal opportunities employer and welcomes applications from all sections of the community.

PhD Opportunity 2: Mathematical and Statistical Modelling of Risk of Emergence of Antimicrobial Resistant Bacterial Pathogens and Reservoirs in Agricultural Slurry

We are now advertising for a second PhD opportunity – this one joint with Theodore Kypraios in the School of Mathematics – and linked to the EVAL-FARMS project.

PhD Studentship – Mathematical modelling of antimicrobial resistance

Closing Date
Tuesday, 31st January 2017

Supervisor: Dov Stekel (School of Biosciences)

Secondary Supervisor: Theodore Kypraios (School of Mathematical Sciences)

Research Title
Mathematical and Statistical Modelling of Risk of Emergence of Antimicrobial Resistant Bacterial Pathogens and Reservoirs in Agricultural Slurry

Research Description

Antimicrobial resistance (AMR) is major global challenge to human and animal health and welfare. This project will develop mathematical models that can be used to assess the risk of emergence of bacterial strains, including pathogens, with new combinations of antimicrobial resistant genes, and the impact of different farm interventions on these risks. The models will be fitted to experimental data derived from the EVAL-FARMS project, which is assessing the possible selection for antimicrobial resistant bacteria in slurry from the University of Nottingham’s dairy farm. Specifically, the project will develop hybrid deterministic/stochastic population models that can describe large populations of many different strains of bacteria, but with rare, random events to describe the acquisition, loss or transfer of a resistance of or between bacterial strains. Similar models have been successfully used in describing virus dynamics. Data fitting will use advanced Bayesian techniques, including Markov Chain Monte Carlo, and approximate Bayesian techniques where experimental data is partially observed. The probabilistic framework will then be used to associate levels of risk to emergence of new resistant strains, and to assess the impact of different farm interventions.

Award Start Date: 25/09/2017

Duration of Award: 48 months

Applicant Qualification Requirements

At least a 2.1 (Hons) degree or equivalent in a relevant quantitative subject, e.g. mathematics, statistics or physics. For EU students, English Language IELTS scores of at least 6.5 (no less than 6.0 in any element). A Masters degree in a relevant subject would be desirable but not essential. Computer programming skills in a relevant language, e.g. C/C++, Python, R or Matlab would be an advantage. The award is available for UK or EU students only.

How to Apply

Please apply on-line at naming Dov Stekel as the main supervisor and using the title and research description from this advertisement.

I stand by the expertise of my academic colleagues, regardless of their Nationality

On this I have to make a public stand.

Expertise knows no national boundaries. One of the privileges of academic life is the opportunity to work with exceptional people from all over the world. We judge knowledge not by the colour of the skin (or passport) of the person who shares it with us, but with its ability to explain and predict the natural or human world.

I am fortunate enough to be part of a team of exceptionally talented researchers trying to understand the spread of antimicrobial resistance in agriculture. Part of our public remit – we are funded by tax-payers money – is to help inform government policy. Our team comprises of of British people, EU Nationals, and people from outside the EU. We are building broad collaborations with people in many countries – antimicrobial resistance also knows no national boundaries – and is a threat to everyone in the world.

I publically stand against the notion that somehow because some people have come from different countries that they are less qualified to advise the UK government. Dangerous, racist, appalling. After fleeing the Nazi annexation of Austria in 1938, by grandfather was locked up by the British government as an “enemy alien”. Is this what we are returning to?

PhD Opportunity: Geospatial modelling the spread of antimicrobial resistance in the environment

We are looking for an excellent candidate for a PhD in Geospatial modelling the spread of antimicrobial resistance in the environment, funded by the NERC Envision doctoral training programme, supervised jointly by myself, Stuart Marsh (Nottingham Geospatial Institute), Malcolm Bennett (School of Veterinary Medicine and Science) and Andrew Singer (Centre for Ecology and Hydrology). Details of the project are below. Please apply by 6th January on

Project Description

Antimicrobial resistance (AMR) is a major global challenge. It is estimated that globally 700,000 human deaths per year are due to AMR, predicted to rise to 10 million by 2050. While much research is in medical/agricultural contexts, the spread of AMR in the environment is often neglected. Antimicrobials and antimicrobial resistant genes (ARGs) and organisms have sources in agriculture and wastewater treatment plants (WWTP), which are spread on land through slurry, manures or sewage sludge, or released directly into rivers. Soil and water polluted by antimicrobials and resistant bacteria can impact crops, animals and humans. Thus, AMR presents both an environmental and human health hazard.

Our vision is to develop mathematical models that can predict AMR spread in the environment. Such modelling will require numerous factors, including: prevalence of ARGs and the relative role of different AMR sources, pathways, drivers and receptors. These models would be used to inform policy on the priorities for controlling AMR in agriculture and the wider natural environment and on the most appropriate specific actions following an outbreak of an AMR pathogen. They will also help prioritise AMR surveillance. Most mathematical modelling for the environmental spread of AMR operates locally, e.g. in a slurry tank, field soil or a WWTP, or a smaller still, e.g. a biofilm. A challenge is to develop predictive models at much larger environmental scales.

This PhD project will begin to address this challenge, by following four novel modelling approaches: incorporation of the heterogeneity of AMR agents; using a combination of deterministic and stochastic models to account for both microscopic and population level scales; up-scaling the current approaches to an environmental scale by using methods developed for geospatial modelling of pollutants; and calibrating the models with geospatially explicit environmental AMR surveillance data from our projects and those of our collaborators.

Funding Notes

Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in any relevant scientific discipline with considerable quantitative component (mathematics, physics, computer science, engineering). They must be able to evidence excellent mathematical and computer programming skills, a willingness to work across multi-disciplinary boundaries, including physical geography and microbiology.

Full studentships are available to UK/EU candidates who’ve been ordinarily resident in the UK throughout the 3-year period immediately preceding the date of an award. EU candidates who’ve not been resident in the UK for the last 3-years are eligible for “tuition fees-only” awards (no maintenance grant).