Open Positions: Early Career Research Fellowships in systems biology/machine learning for food and disease

Salary: £31,656 - £37,768 per annum depending on skills and experience.
Closing date: 17 May 2016

The University of Hertfordshire is investing in its future research staff and infrastructure, and is in the process of transitioning the delivery of its research under six Themes: Food; Global Economy; Health and Wellbeing; Heritage, Cultures and Communities; Information and Security; Space. These will assist in the further development of research excellence and provide both increased external profile and internal focus for Hertfordshire’s research activities.

Six new Research Fellow posts are each offered for a five year term in the first instance. It is our expectation, however, that successful appointees will grow their research activities to become permanent academic staff members by the end of that period.

Qualifications required

You must have a first degree in a science, such as biology, computer science, mathematics or a relevant subject, and a doctoral degree in bioinformatics, machine learning, quantitative genetics or a related subject area. Experience in systems biology, big data science or genomics will be particularly relevant.

Research focus and environment

This Fellowship will focus on emerging methods in biocomputation that generate and exploit large data sets of biological information available from genomics, transcriptomics, proteomics and metabolomics to better understand mechanisms of host resistance/immunity and/or resistance breakdown. The Fellow will generate an improved understanding of relevant biological systems to develop specific strategies to combat infectious diseases caused by plant, animal or human pathogens. This Fellowship will be supported by existing collaborations between colleagues in Schools of Life & Medical Sciences (Kukol, Stotz, Barling, Fitt) and Computer Science (Steuber). The Fellow is expected to use the University’s high performance computer cluster.

Experience and skills required for the post

  • Considerable experience with big data analysis and machine learning, including working knowledge of scripting languages like Perl, Python and/or R;
  • Knowledge of genomic research techniques, such as next-generation sequencing, proteomics and/or metabolic profiling;
  • Practical experience with the application of numerical analysis and/or mathematical models to biological datasets, for example in genomics or quantitative genetics;
  • Evidence of original research published in high impact journals.

Research expectations

The Fellow is expected to develop a collaborative research program with our academic partners. We envisage that the Research Fellow will become a permanent staff member, supported by funding from successful research grant applications and developing new areas of teaching, especially at the post-graduate level. To ensure this, the two Schools will provide career training for the Fellow. The Fellow will have established collaborations with companies and successfully obtained co-funded industry-government projects. The Fellow will continue to publish high-impact papers and be leading an internationally recognised research team.

Description of Schools

The Early Career Research Fellow will work with and receive support from the School of Life and Medical Sciences and the School of Computer Science. The successful candidate can build on the strengths of both Schools and may combine experiment-based empirical research with data-based analysis.

Within the School of Life and Medical Sciences, the Centre for Agriculture, Food and Environmental Management (CAFEM) is a research and teaching collaboration with the Royal Veterinary College, Rothamsted Research and Oaklands College. The Fellow will work with researchers in CAFEM who have experience with systems biology applicable to crop protection, combining experimental field and lab research with computational modelling. Within the School of Computer Science, research in the Biocomputation Research Group involves development of computational models to study biological systems and application of biologically-inspired machine learning algorithms for the analysis of "real-world" data. Members of the Biocomputation Group analyse and simulate computational models at different levels of complexity and collaborate closely with leading experimentalists in the UK and abroad.

Informal enquiries are encouraged and should be made to:
Professor Bruce Fitt,
Professor of Plant Pathology,
Tel + 44 (0)1707 284751


Dr. Volker Steuber,
Reader in Biocomputation and Head of the Biocomputation Research Group,
Tel: +44 (0)1707 284350.

Applications should be made through, job reference 013457.

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