www.acad.jobs : academic jobs worldwide – and the best jobs in industry
                
     
Position: Research Fellow in Computational Cancer Biology
Institution: University College London
Department: Division of Biosciences - Research Department: Genetics, Evolution and Environment (UCL Genetics Institute)
Location: London, United Kingdom
Duties: We are seeking an enthusiastic, creative and motivated individual with a keen interest in cancer genomics, clonal evolution and the tumour microenvironment, to explore the mutational processes leading to quiescence and similar cellular states in a variety of cancers. The post holder will be expected to apply various statistical modelling, machine learning and data integration approaches on bulk and single cell genomics and transcriptomics datasets to uncover the mutational and immunological triggers of tumour dormancy. The role will involve the development of new approaches to model and link mutational signatures, clonal evolution and tumour microenvironment interactions in the context of temporary cell states in cancer
Requirements: Applicants must have a PhD in a relevant subject area, e.g. computational biology, bioinformatics, statistics, computer science, mathematics, physics, genetics, biology, biotechnology or similar subject; and extensive programming experience, preferably (but not exclusively) in at least one of the following languages: R, Python, C/C. Previous experience with NGS data and/or cancer is welcome but not mandatory. The applicants must demonstrate that they have a keen interest in mutational processes, tumour evolution and/or modelling the tumour microenvironment
   
Text: Research Fellow in Computational Cancer Biology, - Ref:1877701 Click here to go back to search results Apply Now UCL Department / Division Division of Biosciences Specific unit / Sub department Research Department: Genetics, Evolution and Environment (UCL Genetics Institute) Location of position London Grade 7 Hours Full Time Salary (inclusive of London allowance) £36,028 - £43,533 per annum Duties and Responsibilities Applications are invited for a Research Fellow in Computational Cancer Biology to join the Secrier lab (https://secrierlab.github.io/) on a UKRI Future Leaders Fellowship-funded post in the Department of Genetics, Evolution and Environment. We are seeking an enthusiastic, creative and motivated individual with a keen interest in cancer genomics, clonal evolution and the tumour microenvironment, to explore the mutational processes leading to quiescence and similar cellular states in a variety of cancers. The post holder will be expected to apply various statistical modelling, machine learning and data integration approaches on bulk and single cell genomics and transcriptomics datasets to uncover the mutational and immunological triggers of tumour dormancy. The role will involve the development of new approaches to model and link mutational signatures, clonal evolution and tumour microenvironment interactions in the context of temporary cell states in cancer. Rich multi-omics datasets are available in oesophageal adenocarcinoma, sarcoma and brain cancers from collaborators at the Universities of Cambridge, Southampton, Cologne and the UCL Cancer Institute for this purpose. This position is fully computational, but we will collaborate with our wet lab partners for the purpose of validation of the findings. The post is funded for 3 years in the first instance. There is the possibility for a further extension of another 3 years as part of the extension of the grant, conditional on satisfactory outputs from the first 3 years. The starting date for the post is flexible, but ideally between November 2021 and January 2022. Key Requirements Applicants must have a PhD in a relevant subject area, e.g. computational biology, bioinformatics, statistics, computer science, mathematics, physics, genetics, biology, biotechnology or similar subject; and extensive programming experience, preferably (but not exclusively) in at least one of the following languages: R, Python, C/C . Previous experience with NGS data and/or cancer is welcome but not mandatory. The applicants must demonstrate that they have a keen interest in mutational processes, tumour evolution and/or modelling the tumour microenvironment. Please note: Appointment at Grade 7 is dependent upon having been awarded a PhD; if this is not the case, initial appointment will be at research assistant Grade 6B (Salary £31,542 - £33,257 per annum, including London Allowance) with payment at Grade 7 being backdated to the date of final submission of the PhD thesis (including corrections). Further Details Further particulars, including a job description and person specification, can be accessed at the bottom of this page. Please ensure you read these carefully before applying for the post. To apply for the vacancy please click on the 'Apply Now' button below. Please make sure you include a CV and cover letter outlining your motivation to join the Secrier research group in the application. Interviews are expected to be held in August 2021. For informal enquiries about the post please contact Dr Maria Secrier at m.secrier@ucl.ac.uk If you have any queries regarding the application process please contact Biosciences staffing on biosciences.staffing@ucl.ac.uk quoting the vacancy reference number: 1877701 UCL Taking Action for Equality We will consider applications to work on a part-time, flexible and job share basis wherever possible. Closing Date 16 Aug 2021 Latest time for the submission of applications 23:59 Interview date August 2021 Our department holds an Athena SWAN Bronze award, in recognition of our commitment to advancing gender equality. This appointment is subject to UCL Terms and Conditions of Service for Research and Support Staff. Please use these links to find out more about UCL working life including the benefits we offer and UCL Terms and Conditions related to this job. Job description & Person Specification Apply Now
Please click here, if the job didn't load correctly.
Your browser does not support iframes. Please click <a href="https://www.acad.jobs/job.php?t_id=J000349969&redirect" target="_parent" style="color:#7A7A7A">here</a>, if the job didn't load correctly.