Postdoc position on interpretable machine learning at Cardiff University

Applications are invited for a Postdoctoral Research Associate post in Cardiff University’s School of Computer Science & Informatics.
Keywords: vector space semantics, deep learning, interpretable machine learning, symbolic models
Overview: This is a full-time, fixed-term post for 30 months, starting on 1 May 2017 or as soon as possible thereafter. The successful candidate will be dedicated to finding creative solutions and have a genuine curiosity and enthusiasm to undertake world-class research in the field of Machine Learning / Artificial Intelligence. Specifically, the aim of this post will be to develop novel methods for learning interpretable/symbolic models from diverse sources of information, including knowledge graphs, vector space models and natural language text. These models will then be used as background theories in applications such as recognising textual entailment, automated knowledge base completion, or zero-shot learning. You will work closely with Steven Schockaert. You will possess or be near the completion of a PhD in Computer Science or a related area, or have relevant industrial experience. This research will be part of the FLEXILOG project, which is funded by the European Research Council (ERC)
Closing date for applications: 2 March 2017
Essential criteria:
1. Postgraduate degree at PhD level, or near to completion of a PhD in a related subject area or relevant industrial experience
Knowledge, Skills and Experience.
2. An established expertise and proven portfolio of research and/or relevant industrial experience within at least two of the following research fields: Machine Learning, Knowledge Representation, Natural Language Processing.
3. A strong background in statistics and linear algebra.
4. Excellent programming skills.
5. Knowledge of current status of research in specialist field.
6. Proven ability to publish in relevant journals (e.g. Artificial Intelligence, Journal of Artificial Intelligence Research, Journal of Machine Learning Research, Machine Learning) or top-tier conferences (e.g. IJCAI, AAAI, ECAI, NIPS, ICML, KDD, ACL, EMNLP).
7. Ability to understand and apply for competitive research funding.
8. Proven ability in effective and persuasive communication.
9. Ability to supervise the work of others to focus team efforts and motivate individuals.
10. Proven ability to demonstrate creativity, innovation and team-working within work.
Background about the university:
Cardiff is a strong and vibrant capital city with good transportation links and an excellent range of housing available. Various surveys have ranked it as one of the most liveable cities in Europe. Cardiff University is a member of the Russell Group of research universities, and was ranked 5th in the UK based on the quality of research in the 2014 Research Evaluation Framework. The university has a successful School of Computer Science & Informatics with an international reputation for its teaching and research activities. The school has a strong research track record recognised for its outstanding impact in terms of reach and significance, with 79% of its outputs deemed world-leading or internationally excellent in the 2014 Research Excellence Framework.
Background about the project:
Vector space embeddings have become a popular representation framework in many areas of natural language processing and knowledge representation. In the context of knowledge base completion, for example, their ability to capture important statistical dependencies in relational data has proven remarkably powerful. These vector space models, however, are typically not interpretable, which can be problematic for at least two reasons. First, in applications it is often important that we can provide an intuitive justification to the end user as to why a given statement is believed, and such justifications are moreover invaluable for debugging or assessing the performance of a system. Second, the black box nature of these representations makes it difficult to integrate them with other sources of information, such as statements derived from natural language, or from structured domain theories. Symbolic representations, on the other hand, are easy to interpret, but classical inference is not sufficiently robust (e.g. in case of inconsistency) and too inflexible (e.g. in case of missing knowledge) for most applications.
The overall aim of the FLEXILOG project is to develop novel forms of reasoning that combine the transparency of logical methods with the flexibility and robustness of vector space representations. For example, symbolic inference can be augmented with inductive reasoning patterns (based on cognitive models of human commonsense reasoning), by relying on fine-grained semantic relationships that are derived from vector space representations. Conversely, logical formulas can be interpreted as spatial constraints on vector space representations. This duality between logical theories and vector space representations opens up various new possibilities for learning interpretable domain theories from data, which will enable new ways of tackling applications such as recognising textual entailment, automated knowledge base completion, or zero-shot learning.
More information:
For more details about the project and instructions on how to apply, please go to www.cardiff.ac.uk/jobs and search for job 5545BR. Please note the requirement to evidence all essential criteria in the supporting statement.