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Position: Machine Learning/Text Mining Scientist
Institution: European Molecular Biology Laboratory
Location: Hinxton, United Kingdom
Duties: The successful candidate will be responsible for developing machine learning and related methods to extract target (gene/protein)-disease relationships and other entities from research publications and deliver these to the Open Targets Platform. You will be challenged with improving the precision and recall of the current target-disease associations we deliver to the platform, potentially refining these results further through NLP and sentiment analysis, and developing new methods for the extraction of new entities, according to the requirements of the Open Targets Platform
Requirements: Higher degree, preferably PhD, in a the area of life sciences and/or text mining and machine learning; Experience in Java and Python. familiar with Perl is a plus; Proven experience of a range of techniques in the areas of NLP, deep learning, machine learning, such as named entity recognition, text classification and information extraction; Application of these skills within biology/biomedical domain, in an academic, industrial or publishing settings; Evidence of applications you have developed, with clarity on your specific contributions; Previous experience working with full text XML documents, preferably in the life sciences; Flexible approach and ability to take on new skills; Self starter; Team player and good communicator - written and verbal
   
Text: Machine Learning / Text Mining Scientist Location: EMBL-EBI, Hinxton near Cambridge, UK Staff Category: Staff Member Contract Duration: 3 years Grading: 5 or 6 (monthly salary starting at £2,631 or £2,944 after tax) Closing Date: 8 April 2019 Reference Number: EBI01372 We are seeking to recruit an expert with experience in entity recognition, NLP and machine learning to work on text mining workflows within the Open Targets Platform. This position will be located within the Literature Services Team at the European Bioinformatics Institute (EMBL-EBI), and will work in close collaboration with Open Targets team, also based at the Wellcome Genome Campus. The Open Targets Platform supports workflows for target validation by gathering and integrating various scientific evidences to support decision making around drug development. Evidence in the Open Targets Platform includes de novo experimental data such as gene expression and variation data, as well as data residing in public data resources such as protein-protein interaction data and evidence for target-disease associations mined from research publications. This position will focus on the extraction of target-disease associations from abstracts and full text publications available in Europe PMC, for consumption by the Open Targets Platform, improving the precision and recall of existing workflows and extending the extraction of concepts to other entities such as drugs or mutations. Your role The successful candidate will be responsible for developing machine learning and related methods to extract target (gene/protein)-disease relationships and other entities from research publications and deliver these to the Open Targets Platform. You will be challenged with improving the precision and recall of the current target-disease associations we deliver to the platform, potentially refining these results further through NLP and sentiment analysis, and developing new methods for the extraction of new entities, according to the requirements of the Open Targets Platform. You will work within the context of the Literature services multi-disciplinary team, which includes full stack developers, ontology experts, data scientists, and biologists as well as text mining and machine learning scientists. This is a great opportunity for someone who wants to make an impact with their text and data mining skills in an open research data infrastructure. Specific job responsibilities include: Scientific requirements gathering Point of contact for the project with the Open Targets team Understand the scientific drivers and context of the project Developing and benchmarking prototype core text and data mining algorithms Application of methods to large datasets (millions of abstracts/full text articles) Writing reports and publications, giving presentations, as required You have Higher degree, preferably PhD, in a the area of life sciences and/or text mining and machine learning; Experience in Java and Python. familiar with Perl is a plus; Proven experience of a range of techniques in the areas of NLP, deep learning, machine learning, such as named entity recognition, text classification and information extraction; Application of these skills within biology/biomedical domain, in an academic, industrial or publishing settings; Evidence of applications you have developed, with clarity on your specific contributions; Previous experience working with full text XML documents, preferably in the life sciences; Flexible approach and ability to take on new skills; Self starter; Team player and good communicator - written and verbal. Why join us At EMBL-EBI, we help scientists realise the potential of ‘big data’ in biology by enabling them to exploit complex information to make discoveries that benefit mankind. Working for EMBL-EBI gives you an opportunity to apply your skills and energy for the greater good. As part of the European Molecular Biology Laboratory (EMBL), we are a non-profit, intergovernmental organisation funded by 22 member states and two associate member states. We are located on the Wellcome Genome Campus near Cambridge in the UK, and our 600 staff are engineers, technicians, scientists and other professionals from all over the world. EMBL is an inclusive, equal opportunity employer offering attractive conditions and benefits appropriate to an international research organisation. The remuneration package comprises a competitive salary, a comprehensive pension scheme and health insurance, educational and other family related benefits where applicable, as well as financial support for relocation and installation. We have an informal culture, international working environment and excellent professional development opportunities but one of the really amazing things about us is the concentration of technical and scientific expertise - something you probably won’t find anywhere else. If you’ve ever visited the campus you’ll have experienced first-hand our friendly, collegial and supportive atmosphere, set in the beautiful Cambridgeshire countryside. Our staff also enjoy excellent sports facilities including a gym, a free shuttle bus, an on-site nursery, cafés and restaurant and a library. What else do I need to know To view a copy of the full job description please click here To apply please submit a covering letter and CV through our online system. Applications are welcome from all nationalities and this will continue after Brexit. For more information please see our website. Visa information will be discussed in more depth with applicants selected for interview. EMBL-EBI is committed to achieving gender balance and strongly encourages applications from women, who are currently under-represented at all levels. Appointment will be based on merit alone. This position is limited to the project duration specified. Applications will close at 23:00 GMT on the date listed above.
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