Postdoctoral researcher

A postdoctoral research position is available as part of a collaboration between NaCTeM and the AI research centre ARCHIMEDES Research on AI, Data Science, and Algorithms. The position is available for 24 months to advance research on emotion detection and LLMs for mental health.

Skills: deep learning for NLP; strong theoretical background and extensive coding experience in deep learning for NLP, prompting, fine-tuning LLMs, RAG, and other current methodologies for generative AI and LLMs.

Location: Athens, Greece.

Closing date: Open until filled

To apply: Please send your CV and publications to

PhD opportunity in collaboration with Athens Univ. of Economics and Business

We are pleased to announce a PhD opportunity in collaboration with Ion Androutsopoulos Department of Informatics, Athens University of Economics and Business, under the auspices of ARCHIMEDES Research on AI, Data Science, and Algorithms.

Towards finance market prediction using fine-grained evaluation of LLMs.

Leveraging LLMs in the financial domain presents numerous advantages ranging from data analysis and extraction, risk assessment, stock market prediction, fraud detection, regulatory compliance and customer support, among others. Integrating LLMs in the financial domain enhances efficiency and scalability of processes. An example of a finance market prediction task is stock market prediction. Its performance depends on a subtle interplay of dimensions such as sentiment, emotions, stance, argumentation, uncertainty and temporality. To improve model performance, fine-grained evaluation approaches which are able to explore not only the interplay between dimensions, but also different prompting strategies, domain specific and common-sense knowledge, will be examined. This project aims to refine the integration of LLMs in market prediction and trading by leveraging PIXIU, an advanced framework tailored for the financial domain. PIXIU encompasses a suite of specialized financial LLMs, a versatile multi-task and multi-modal instruction dataset, and a comprehensive evaluation benchmark. This involves examining different prompting strategies and integrating both domain-specific knowledge and broader common-sense information to improve predictions and trading strategies. The goal of the PhD is to achieve higher efficiency and scalability in market prediction and trading processes, ultimately leading to more informed decision-making and better outcomes in the financial sector.

Entry requirements: First-class bachelors or MSc in Computer Science or international equivalent.

Skills: Deep Learning for NLP; prompting, fine-tuning LLMs, RAG, and other current methodologies for generative AI and LLMs; excellent coding skills, ability to analyse large volumes of data, perform statistical analyses, and assess the effectiveness of interventions.

Publications: One or two publications as first author in top-tier NLP/ML/AI conferences (ACL, EMNLP, NAACL, AAAI, NeurIPS)

The successful candidate will have the opportunity to spend time at the National Centre for Text Mining, University of Manchester

Interested candidates should send their CV to The position will remain open until filled.

iCASE EPSRC funded PhD- multimodal NLP - University of Manchester & BAE

We are seeking an enthusiastic PhD candidate to work in multimodal NLP for model-based systems engineering.


This project is funded by the EPSRC iCASE (sponsored by BAE Systems) to conduct research in the area of multimodal natural language processing (NLP) for model-based systems engineering based on Large Language Models (LLMs). LLMs have demonstrated a remarkable ability to generate text when presented with images, text, audio and video as input. They are able to achieve higher performance than traditional neural methods and pre-trained language models, without the need for supervised training.

The project will examine different approaches to multimodal LLM-based NLP to address complex and fine-grained tasks such as reasoning in model-based systems engineering. The PhD will delve into LLM architectures, data augmentation methods, multi-task and domain-specific LLMs, prompting engineering and interpretability.

The candidate will have the opportunity to work with experts at BAE to gain experience in the practical application of model-based systems engineering. The candidate will join the world-class teams of Prof. S. Ananiadou (Computer Science and National Centre for Text Mining, Natural Language Processing, LLMs) and Prof. H. Yin (Electrical and Electronic Engineering, Deep Learning, Computer Vision).


You will have a very good undergraduate degree in Computer Science (minimum 2:1 UK or equivalent for EU students). Experience and knowledge of NLP, multimodal LLMs, Ontologies, Semantic Web, Computer Aided Engineering (CAE) and Model-Based Systems Engineering (MBSE) tools and technology will be considered as an advantage.

The successful candidate must be capable of obtaining UK security clearance to fulfil any onsite industrial placement at the location of the host site.

Research Environment in Host Institution

The Department of Computer Science at the University of Manchester (UoM) is in the unique position of hosting the National Centre for Text Mining (NaCTeM), the first publicly funded centre for text mining in the world, focusing on fundamental research in Natural Language Processing (LLMs, interpretability, information extraction) in a variety of domains. Besides NaCTeM, academic expertise in AI is spread across a number of other institutes including the Institute for Data Science and AI (IDSAI), the Centre for AI Fundamentals and partnerships with the Alan Turing Institute and the European Laboratory for Learning and Intelligent Systems (ELLIS).

BAE Systems

BAE Systems provides some of the world''s most advanced, technology-led defence, aerospace and security solutions. They employ a skilled workforce of more than 93,000 people in around 40 countries. Working with customers and local partners, they develop, engineer, manufacture, and support products and systems to deliver military capability, protect national security and people, and keep critical information and infrastructure secure.

Before you apply

We strongly recommend that you contact the supervisors of this project prior to application.

How to apply

To be considered for this project, you will need to complete a formal application through our online application portal by the 30th of April 2024.

When applying, you will need to specify the full name of this project, the name of your supervisor, how you are planning to fund your research, details of your previous studies, and the names and contact details of two referees.

Please also send the following to Prof. Sophia Ananiadou ( and Prof. Hujun Yin (

  • Cover letter and full CV
  • Full degree transcripts and relevant certificates

Candidates will be shortlisted by a panel comprising members of UoM and BAE Systems. Selected candidates will be invited to give a presentation followed by a formal interview.

Your application will not be processed unless all of the required documents are submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

If you have any questions about making an application, please contact our admissions team by emailing

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact.

We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We will consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).

Funding Notes

This project is funded through EPSRC iCASE (with BAE Systems). The project will pay the tuition fees and provide a tax free stipend set at the UKRI rate (£18,622). We are able to offer a limited number of studentships to applicants outside the UK. Therefore, full studentships will only be awarded to exceptional quality candidates, due to the competitive nature of this scheme. Additional research funds will be available.

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