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Research Fellow Natural Language Processing

University of Birmingham - School of Government

Location: Birmingham
Salary: £44,414 to £52,841 p.a. Grade 8
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 6th June 2023
Closes: 30th September 2023
Job Ref: 101731

Position Details

Full time starting salary is normally in the range £44,414 to £52,841 with potential progression once in post to £59,450

Fixed term contract up to 3 years

Closing date: 30th September 2023

We will start reviewing applications on 20 June and invite shortlisted candidates for an interview on a rolling basis. If all positions are filled prior to the final closing date (30 September), we may close the advert, so we encourage early applications. 

Application Process:

To apply, please submit the following materials:

  • Cover letter, detailing your research interests and motivation for applying.
  • Curriculum vitae, including a list of publications.
  • Two representative publications or preprints.
  • Contact details for two academic referees.

Research Project:

The Fellow is expected to primarily contribute to the Centre’s research area in understanding and modelling causal relationships in natural language. Understanding causal language is crucial for advancing the fields of NLP and AI more broadly, as it allows for more accurate and nuanced interpretation of human communication. By identifying and modelling the causal relationships present in language, NLP and AI systems can gain a deeper understanding of the underlying structure and meaning in texts, enabling more accurate predictions, inference, and decision-making. This is particularly important when dealing with complex, real-world problems, where causality plays a key role in shaping events and outcomes. Furthermore, improved understanding of causal language can enhance the explainability and transparency of AI systems, as it can reveal the rationale behind their predictions and recommendations. This, in turn, can help build trust in AI systems, facilitating their adoption across various domains and fostering more effective human-AI collaboration. Overall, understanding causal language is essential for unlocking the full potential of NLP and AI technologies, enabling them to better comprehend and interact with the intricate and causally-driven world in which we live.

Beyond foundational NLP research, the Centre is also interested in developing applications of causal language models. For example, attribution of credit and blame in political communication, public policy and financial markets analysis, legal reasoning, and misinformation and disinformation detection.

Key Responsibilities:

  • Conduct innovative research on causal language analysis, contributing to the advancement of state-of-the-art methods and tools.
  • Develop novel methodologies and techniques for detection and analysis of causal relationships in text data.
  • Pursue personal research including developing research ideas and winning support, including financial support.
  • Plan, publish and execute high quality research.
  • Project manage research activities and supervise other research staff.
  • Present findings in high quality publications and conference proceedings.
  • Supervise and examine PhD students, both within and out with the University.
  • Provide expert advice to colleagues and students within discipline.
  • Contribute to the administration/management of research in the Centre.

Person Specification:

  • PhD in Computer Science, Artificial Intelligence, Computational Linguistics, or a related field.
  • Extensive research experience and scholarship in natural language processing, machine learning, and deep learning techniques.
  • Extensive experience of applying and/or developing and devising successful models, techniques and methods.
  • Extensive experience in advanced statistical methods and data visualisation techniques.
  • Extensive experience and demonstrated success in planning, undertaking and project managing research to deliver high quality results.
  • Extensive experience and achievement in knowledge transfer, enterprise and similar activity.

Please direct any questions about the position to Professor Slava Jankin at v.jankin@bham.ac.uk.

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