Project Title: Towards Machine Understanding of Human Languages: AI for Storytelling Across Languages and Cultures
PhD Studentship: R-LINCS2 funded. The Studentship is available for a June, October 2023, or February 2024 start.
A PhD studentship that comprises tax-free stipend of £17,668 (increasing in line with UKRI per annum) per year over 3.5 years, tuition fees paid, and a generous study package (e.g. limited research consumables, travel budget, and training when appropriate).
The pan-University Graduate School offers an integrated training programme to the postgraduate community within a single centre, serving to inculcate interdisciplinary working in our next generation of researchers.
Interviews are likely to be online in August 2023.
Project Description: In the realm of AI and computer science, disciplines such as Natural Language Processing (NLP), Human-Computer Interaction (HCI), and AI-centric Language Models are critical for fostering communication and collaboration between humans and machines in the future. Despite the progress made in AI and Machine Learning, existing models still struggle to achieve a deep and precise grasp of natural languages. Although there has been remarkable progress in developing more efficient models for various NLP tasks, Deep NLP models, transformers, and large-scale language models like ChatGPT still face a significant challenge in generating engaging and coherent stories. The existing storytelling models suffer from a lack of coherence and contextuality, which can be attributed to the narrow focus on horizontal sequence-based processing. Storytelling models face challenges in coherence and contextuality due to their emphasis on horizontal sequence-based processing and struggle with narrative intricacies, demanding the incorporation of innovative AI techniques for a deeper grasp of narrative components.
Enhancing modern Deep NLP techniques requires exploring and capturing the complex interactions among words, considering diverse categories of orthographic and phonological relations, as well as the relationships between words and their contextual surroundings across a broad spectrum of texts. Through meticulous scrutiny of the multifarious ways in which words are employed and their interconnectivity, a more profound comprehension of the underlying implications and concepts can be attained. Utilising these insights enables the enhancement of current approaches. The project aims to improve machine understanding of natural languages by developing novel methods for AI-driven storytelling. The work will be focused on designing and developing methods and techniques to analyse and explore large textual datasets and lexical graph databases, discover, and extract the hidden pattern and relationship between words within a language and automatically generate stories describing and explaining those relationships. The project will combine and incorporate approaches from various disciplines including NLP, Deep Neural Networks, Computational Reasoning, Computational Linguistics, Generative AI, Graph Learning and Graph Neural Networks.
Supervisory Team: The candidate will be supervised within the School of Design and Informatics by Dr Javad Zarrin, Dr Kean Lee Kang, and Dr Darshana Jayemanne. Queries on this project should be directed to Dr. Javad Zarrin (j.zarrin@abertay.ac.uk).
Entry Requirements: Candidates must have, or expect to obtain, a first-class or upper second-class honours degree or international equivalent (preferably with a recognised master's degree) in Artificial Intelligence, Data Science, Computer Science, Mathematics, or a closely related discipline.
The successful candidates will have a solid background in AI, with experience spanning machine learning, graph learning, natural language processing, and generative AI. Experience in designing and conducting AI-driven research, coupled with a thorough grasp of research methodologies, AI-related mathematical concepts, and practical skills in Python and data analysis techniques, is highly regarded. Familiarity with storytelling and gaming is a plus but not a mandatory requirement.
For applicants who are non-native speakers of English, the University requires an IELTS of 6.5 (with no band less than 6.0) or an equivalent qualification accepted by the Home Office.
Applications and closing date: 6th August 2023
Applicants should submit through the Abertay University jobs page https://www.abertay.ac.uk/about/working-at-abertay/jobs/, submitting a personal statement of application detailing why you are interested in undertaking this project, and a CV. If you are selected for an interview, you will be required to complete an online Research Student Application Form which includes the submission of a research proposal. Guidance on how to write the proposal can be found here: https://www.abertay.ac.uk/study-apply/how-to-apply/how-to-apply/, Applicants are strongly encouraged to contact Dr. Javad Zarrin (j.zarrin@abertay.ac.uk) for advice on developing a proposal prior to submitting it.
Abertay University was named The Times and The Sunday Times University of the Year for Teaching Quality 2021. According to the results of the Research Excellence Framework 2021, Abertay recorded 60% of its research judged as 'internationally excellent' or 'world-leading', a 23% increase since the last REF2014 – the biggest climb of any Scottish university. Abertay was the first University in the world to offer a degree in games and was top ranked in Europe for both Undergraduate and Postgraduate courses in 2019.
We hold an Athena SWAN Institutional Bronze award and were the first Scottish university to achieve the Race Equality Charter Mark.
Entry requirements
Essential requirements:
Desirable requirements (but not essential)
First-class or upper second-class honours degree (or international equivalent) in artificial intelligence, data science, computer science, mathematics, or a closely related discipline.
A master's degree in computer science, artificial intelligence, machine learning, or a related field, preferably with a research component.
Hands-on programming skills and experience in Python, particularly in its data analytics and machine learning libraries. Experience with deep learning libraries such as TensorFlow or PyTorch.
Proficiency in other programming languages such as C++, Cuda or OneAPI, or other libraries for GPU parallelisation for developing high-performance AI applications.
Demonstrated experience in machine learning, deep learning, NLP, graph learning, GANs, transformers, or language models through projects, internships, or research.
Expertise in working with large-scale graph datasets and proficiency in converting tabular datasets into graph representations, and vice versa.
Good scientific writing, previous research experience.
Publications in peer-reviewed reputable conferences or journals related to AI, machine learning, NLP, or related fields.
A firm grasp of AI mathematical concepts, including linear algebra, calculus, probability, and statistics.
Experience and knowledge in Gamming and storytelling.
Ability to work independently and collaboratively in a research environment.
Experience in implementing AI techniques in interdisciplinary domains or practical applications.
Good interpersonal and communication skills.
Proficiency and expertise in recurrent neural networks, speech recognition, and language processing.
Applicants who are non-native speakers of English, the University requires IELTS of 6.5 (with no band less than 6.0) or an equivalent qualification accepted by the Home Office