Natural Language Processing Engineer - Nuritas
Who We Are
Nuritas™ is a Dublin-based biotech company revolutionizing the discovery of novel and natural active ingredients with scientifically proven health benefits. Our disruptive approach to discovery provides access to the most health-benefiting components within foods by combining artificial intelligence and DNA analysis to data-mine billions of molecules.
This is a great opportunity join an exciting interdisciplinary team working on cutting edge therapeutic development from initial concept to full delivery.
In this role, you will join a project extracting structured information from large volumes of unstructured academic literature using state-of-the-art techniques in Natural Language Processing methods. The goal is to represent and exploit this information using novel knowledge representation technologies.
This is an exciting role that sits at the very beginning of our core predictive technology. It not only shapes the predictive models for our therapeutic peptides, but also contributes to the understanding of work in our laboratory, in-vitro and in-vivo experiments.
The successful graduate will join this 18 month project in a supportive research partnership with Queen’s University Belfast (academic supervisor: Dr Barry Devereux, Lecturer in Artificial Intelligence and Data Analytics). The successful graduate can also choose to undertake a fully funded post graduate qualification in Business & Management as part of the employment package.
- Master’s degree from an accredited program in Computer Science or PhD in computer science/Math/statistics or related fields.
- 1+ years of professional, hands-on experience with modern methods in NLP, information retrieval, or text analysis.
- Legally permitted to work in the EU.
- Experience with the state-of-the-art in Natural Language Processing methodologies (e.g. large-scale LSTM or transformer architectures).
- Experience in knowledge representation and information retrieval (e.g. knowledge graphs, ontologies, search engines).
- Knowledge and good hands-on experience of common machine learning frameworks (e.g. sklearn, Tensorflow, Keras, Pytorch).
- Exposure to software engineering techniques (i.e. experience with version control, best practices in software design).
- Ideally experience with biological or healthcare data or analysis of scientific research articles.
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