英国基因组分析中心招收计算生物学博士后

Postdoctoral Research Assistant in Computational Bioloogy : Norwich, United Kingdom

We are looking for an enthusiastic and motivated individual with PhD to participate in studying integrative and systems-level biological problems in the frame of joint IFR-TGAC projects as a post doctoral research assistant for 5 years.

The post holder will work both in TGAC, a computational biology institute excellent in sequence analysis and large-scale data management, and in IFR, a research institute with substantial experimental resources to focus on intestinal gut biology and food-borne bacteria that cause human disease. The research work will be based on joint IFR-TGAC projects, in particular, those that are initiated within the Gut Health and Food Safety ISP at IFR.

The post holder will have plenty of opportunities to learn novel techniques, programing languages, data management technologies, interact with researchers with different background and work with cutting edge approaches. Also, the post holder could participate in trainings and receive direct advices to develop to be a successful and happy independent researcher.

The research work will focus on the bacterial regulation of human processes, such as autophagy. In the gut, this host-pathogen interaction is important for intestinal homeostasis and autophagy malfunction is related to inflammatory bowel disease (IBD), which often leads to cancer progression. Better understanding the effect of certain bacterial species on the regulation of human intestinal autophagy could help to propose IBD and colon cancer prognosis markers. Additionally, the results could augment the efficacy of currently available probiotics and point out the pitfalls of specific antibiotics treatments on the intestinal homeostasis.

The main activity will be to work with Tamas Korcsmaros and his future colleagues in developing computational analysis and resources by integrating a variety of complex network data (protein-protein interactions, signalling and regulatory interactions), RNAseq, proteomics and metabolomics datasets.
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