Ms A. Brady
No more applications being accepted
Competition Funded PhD Project (Students Worldwide)
About the Project
Research Topic
One of the main problems of inter-language, inter-domain and inter-modal portability is the large amount of labelled data required to train machine learning models in these scenarios, and the cost of acquiring this data. This PhD student will explore the use of unsupervised and semi-supervised machine learning approaches to address this problem and in particular will investigate the use of active learning and representation learning. Active learning enables machine learning models to be trained using reduced amounts of labelled data, while representation learning allows large amounts of unlabelled data to be leveraged in the training process.
This post is within the "Platform Theme A: Understanding Global Content" of the SFI-funded ADAPT centre at Dublin Institute of Technology. This theme focuses on researching robust, domain-agnostic, linguistic analysers, which can be applied to any language and are informed by cues from non-linguistic sources.
Application Procedure:
For further information and informal contact, please email [Email Address Removed]. Please apply via email to [Email Address Removed], including:
• A targeted cover letter (600-1000 words) expressing your suitability for a position
• A complete CV
• Please include the reference code UG_PhD3 in the subject of your email
Funding Notes
Benefits: Payment of tax free stipend and academic fees
References
Requirements:
The successful candidate will have an excellent academic record (first class or II.1 primary degree) in Computer Science or a related discipline, with existing expertise in machine learning. Knowledge of active learning and/or representation learning would be a distinct advantage. They will be highly motivated, with strong written and oral communication skills. They must be eager to work in and learn from multi-disciplinary and multi-organisation teams. They should have English language certification if English is not their first language, the requirement being: IELTS: 7.0+, TOEFL iBT: 100+, TOEFL pBT: 600+, CEF: C1+, or equivalent.