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瑞典优密欧大学计算生物学方向博士后职位招聘
文章来源:知识人网整理       更新时间:2018年11月14日

  Postdoctoral position in computational biology: Decoding plant stress responses with network science methods

  Chemical Biological Centre (KBC) at Umeå University and Swedish University for Agricultural Sciences

  The Department of Physics is looking for an exceptional Postdoc to join our interdisciplinary group at Integrated Science Lab, IceLab, where we work on solving life science problems with tools from physics, mathematics, and computer science. This two-year full-time position is part of a large multidisciplinary 5-year project that also includes researchers at the Chemical Biological Centre (www.kbc.umu.se) and Umeå Plant Science Centre (www.upsc.se), two stimulating research environments at Umeå University.

  Project description and working tasks

  Large amounts of crop and forest production are lost every year to unfavourable weather conditions, and climate change will further increase the losses. To develop plants that maintain yield under adverse conditions, we need a mechanistic understanding of plant stress tolerance. We propose that plant stress responses are mediated by a central regulatory hub, the Mediator complex. The Mediator is an evolutionary conserved multiprotein complex involved in gene expression regulation and chromatin remodelling; essential processes for regulating plant stress responses. It integrates external signals from different receptors by transmitting promoter element-bound transcription factors to RNA polymerase II. Several functions of the Mediator are still unknown, especially how specific Mediator subunits integrate different signalling pathways and how they transfer these signals to RNA polymerase II to regulate the expression of target genes. To reveal the regulatory mechanisms of Mediator and to functionally define stress responses at all organisational levels, we perform module specific analysis of global changes in gene expression, mediator DNA binding, and mediator complex composition at stress. To enable the integration and interpretation of these complex data sets, we will use network methods as well as other computational tools to construct a model of the signalling networks that regulate plant stress responses.

  The postdoctoral project is part of the 5-year multidisciplinary project “Decoding Signalling Networks Controlling Plant Stress Responses”, financed by the Swedish Foundation for Strategic Research. The project provides challenging postdoctoral training for multiple postdocs, who will together solve problems using methodologies from biology, physics, and computer science.

  A successful candidate will study how the Mediator functions as a regulatory hub that integrates stress signals to control changes in gene expression, required for the acclimation to stress. Using a suite of computational tools, the postdoc will identify modules of co-regulated genes with shared regulatory motifs and gene products with conserved biological functions regarding stress responses. The postdoc will be supervised by Martin Rosvall in IceLab, co-supervised by Åsa Strand at Umeå Plant Science Centre and Stefan Björklund at Department of Medical Biochemistry and Biophysics, work in close collaboration with senior research engineer Matilda Rentoft, and be a part of dynamic and multidisciplinary environments, which also provide technical support.

  The position is a two-year full-time employment that will open in March 2019, or at a later date according to an agreement.

  Qualifications

  To qualify for the position, the candidate should have a PhD degree or a foreign degree that is deemed equivalent, in applied mathematics, bioinformatics, computer science, physics, or a relevant field. To be eligible, the degree should have been completed a maximum of three years before the end of the application period unless certain circumstances exist.

  We are looking for a highly motivated and responsible candidate who wants to develop as an individual scientist as well as a research team member. The candidate should have extensive training in statistics or computational modelling with languages such as C++, Python or R, and be passionate about leveraging statistical methods and machine learning algorithms to address biological questions. Working experience in large-scale sequence analysis is an advantage. An ability to perform research independently as well as in a team, and good skills in the English language, are essential.

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