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Bioinformatics Doctoral Research Fellow: Integrative Network models of disease - Translational Bioinformatics

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  • Full or part time
    Prof Hide
    Prof Lawrence
  • Application Deadline
    Applications accepted all year round
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

We invite applications for a PhD studentship in the Department of Neuroscience at the University of Sheffield School of Medicine in the Sheffield Institute for Translational Neuroscience. A stipend and home/EU fees for 3 years will be awarded to the successful candidate. Overseas applicants can apply but have to show the evidence that the difference between the overseas fees and home/EU fees can be covered by other funding.

The Hide Laboratory at the Centre for Genome Translation https://hidelab.wordpress.com is at the Sheffield Institute for Translational Neuroscience (SITraN); a purpose built research facility which was opened in November 2010 by Her Majesty the Queen. The Institute houses computational, clinical and basic scientists with a focus on neurodegenerative disorders such as Motor Neurone Disease (MND) and Parkinson’s disease.

Your aim will be to build integrative methods or datasets that deliver the causative genes, functional modules and networks of interaction between functional modules that delineate the initiation and progression of neurodegenerative diseases such as Alzheimer’s, Amyotrophic lateral sclerosis or Parkinsons. Leveraging large numbers of genomic samples that require interpretation from cohort studies, you will qualify and integrate genomic variants, microarray, RNAseq and protein protein interaction networks to build modules of interacting genes that have a high probability of contribution to the disease phenotype. Using quantitative phenotype and functional and genetic data from subjects, iPSC cell derived models, mouse model and fly models, you will integrate these to effect characterization of known and novel contributing genes.

Working in a stimulating environment with close association with clinical and wet lab scientists, your goal will be to generate testable translatable discoveries. You will be trained and travel to local and international meetings, and you will collaborate closely with the groups at Harvard.

The new lab at Sheffield works in close collaboration with Harvard School of Public Health, Harvard Stem Cell Institute and Biogen Idec in Cambridge, USA and Sheffield departments of Computer Sciences and Neuroscience as well as Sheffield Hallamshire Hospital. In addition we are active members in UK, EU and Global cohort consortia.

You have a high calibre Masters degree (or equivalent qualification) in computational biology, biomedical research, genomics, computer science, mathematics, biostatistics, systems biology or combination of these. You have a strong biological background, and are skilled in scripting with demonstrated understanding of working together with biomedical researchers and quantitative computational biologists. You are confident in aspects of data integration and are enthusiastic about construction and analysis of biological networks and/or data integration. Highly motivated, you wish to work in a stimulating, international, scientific environment. You have excellent interpersonal, written and oral communication skills and seek exposure to a diversity of scientific projects. This PhD will involve close interactions with biological and quantitative collaborators in academia and industry. Interested applicants should send a current curriculum vitae and research statement to Professor Hide by 18 March 2015

Funding Notes

A stipend and home/EU fees for 3 years will be awarded to the successful candidate. Overseas applicants can apply but have to show the evidence that the difference between the overseas fees and home/EU fees can be covered by other funding.

How good is research at University of Sheffield in Allied Health Professions, Dentistry, Nursing and Pharmacy?
Biomedical science

FTE Category A staff submitted: 64.66

Research output data provided by the Research Excellence Framework (REF)

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