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Position: Computational Biologist
Institution: University of Cambridge
Department: Department of Clinical Biochemistry
Location: Cambridge, Cambridgeshire, United Kingdom
Duties: The successful candidate will have the opportunity to contribute to a broad array of experiments involving rich 'omics datasets, multivariate analysis, and machine learning-based approaches, as well as the design and analysis of drug screens and high throughput cellular assays. Candidates with a curiosity about the underlying biology are likely to find this position particularly rewarding, as there are numerous projects to get involved in that could make a substantial contribution to human health. The position offers opportunities for technical and personal growth and career development, including access to professional computational training should the applicant wish to extend their skill set into a new area. Applicants should have
Requirements: Degree to a level of BSc or higher (or equivalent), preferably in computational biology or physical sciences with an emphasis on Data Science, Statistics and related programming with 2+ years relevant computational experience; Mastery of relevant software, pipelines, programming languages (e.g. R, Perl or Python, Unix/Linux and Windows batch scripting); Experience with the analysis of bulk and/or single cell 'omics datasets (e.g. RNA-seq, ATACseq, ChIP-seq, lipidomics, proteomics)
   
Text: Computational Biologist (Fixed Term) We are seeking a computational biologist to join our group's efforts to use human pluripotent stem cells and mouse models to illuminate disease mechanisms and discover potential therapies for obesity and neurodegenerative disease. The role offers an exceptional opportunity to join a friendly and diverse team with expertise in developmental and stem cell biology, neuroscience, gene editing, disease modelling, and computational biology in the laboratory of Dr. Florian Merkle ( www.merklelab.org ). We are based at the highly collaborative environment and world-leading Wellcome Trust-MRC Institute of Metabolic Science ( https://www.ims.cam.ac.uk/ ) at the University of Cambridge. This position is funded by the New York Stem Cell Foundation (NYCSF, https://nyscf.org/ ), and will enable the successful applicant to join an international network of investigators tackling major challenges in human health. Our group is also affiliated with the Cambridge Stem Cell Institute ( https://www.stemcells.cam.ac.uk/ ) and we collaborate closely with the Open Targets academic-industrial partnership ( https://www.opentargets.org/ ), and the UK Dementia Research Institute (UKDRI, https://ukdri.ac.uk/centres/cambridge ). The successful candidate will have the opportunity to contribute to a broad array of experiments involving rich 'omics datasets, multivariate analysis, and machine learning-based approaches, as well as the design and analysis of drug screens and high throughput cellular assays. Candidates with a curiosity about the underlying biology are likely to find this position particularly rewarding, as there are numerous projects to get involved in that could make a substantial contribution to human health. The position offers opportunities for technical and personal growth and career development, including access to professional computational training should the applicant wish to extend their skill set into a new area. Applicants should have: Degree to a level of BSc or higher (or equivalent), preferably in computational biology or physical sciences with an emphasis on Data Science, Statistics and related programming with 2 years relevant computational experience Mastery of relevant software, pipelines, programming languages (e.g. R, Perl or Python, Unix/Linux and Windows batch scripting) Experience with the analysis of bulk and/or single cell 'omics datasets (e.g. RNA-seq, ATACseq, ChIP-seq, lipidomics, proteomics) An ability and desire to work independently once appropriate training and guidance have been provided, and a motivating interest to understand the basis of disease and develop new treatments Outstanding organisational and record-keeping practices (e.g. clear documentation when writing and sharing code), and comfort with independently managing long-term projects with a track record of meeting deadlines Clear oral and written communication skills to allow accurate information to be exchanged, and strong interpersonal skills with a track record of effectively working as part of a team We welcome applications from individuals who wish to be considered for part-time working or other flexible working arrangements. Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online. Fixed-term: The funds for this post are available for 2 years in the first instance. Closing date: 17th July, 2022 Interview date: 20th July, 2022 (To be confirmed) Proposed start date: 5th September 2022 though there is some flexibility. Informal inquiries about this position should be directed to Florian Merkle at fm436@medschl.cam.ac.uk . Please ensure that you upload a covering letter and CV in the Upload section of the online application. The covering letter should outline how you match the criteria for the post and why you are applying for this role. If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application. Please include details of your referees, including email address and phone number, one of which must be your most recent line manager. Please quote reference RG31831 on your application and in any correspondence about this vacancy. The University actively supports equality, diversity and inclusion and encourages applications from all sections of society. The University has a responsibility to ensure that all employees are eligible to live and work in the UK. Further information Further Particulars Apply online Department/Location Department of Clinical Biochemistry Salary £31,406-£40,927 Reference RG31831 Category Academic-related Published 17 June 2022 Closing date 17 July 2022
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