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- Tasks: Join our team to enhance Large Language Models' robustness and explainability.
- Company: Be part of the University of Edinburgh, a top research centre in Computer Science.
- Benefits: Enjoy a collaborative environment with access to cutting-edge technology and research.
- Other info: Experience with Python and deep learning libraries is essential for success.
- Why this job: Work on impactful projects in a world-leading NLP group and contribute to AI advancements.
- Qualifications: PhD (or near completion) in NLP, Machine Learning, or related fields required.
The predicted salary is between 36000 - 60000 £ per year.
The School of Informatics, University of Edinburgh invites applications for a Post-Doctoral Research Associate in Natural Language Processing and Machine Learning to work with Dr Pasquale Minervini in the Edinburgh NLP Group. The project is funded by Huawei via the Huawei-Edinburgh Joint Lab initiative. The researcher will work on projects involving the design and application of improving the robustness and trustworthiness of Large Language Models when solving complex reasoning tasks, while improving their explainability and generalisation properties.
They will be part of the Edinburgh NLP Group, a world-leading research group in Natural Language Processing. The School of Informatics is one of the largest research centres in Computer Science in Europe and is ranked #1 in the UK in terms of research power by a large margin. The Edinburgh NLP Group is consistently ranked among the world’s leading research groups in Natural Language Processing. We offer an exciting opportunity to work in an interdisciplinary, collaborative, friendly, and supportive environment that integrates various fields of Computer Science and Artificial Intelligence.
Your skills and attributes for success:
- PhD (or near completion) in Natural Language Processing, Machine Learning, or a related discipline.
- Experience and evidence of effective independent research work within an interdisciplinary team.
- Demonstrated ability to plan and execute a research project, solve problems independently, and make original contributions to a research field.
- Demonstrated quality of research performance, as evidenced by high-quality publications in top-tier ML and NLP venues (e.g., ACL, EMNLP, NAACL, ICML, NeurIPS, ICLR, AAAI, IJCAI, WWW, KDD, ICDM, and relevant journals).
- Strong programming skills; experience with Python NLP and deep learning libraries (e.g., HF Transformers, PyTorch, Jax, or TensorFlow).
- Ability to communicate complex information clearly, orally and in writing, in English.
Post-Doctoral Research Associate employer: Ellis
Contact Detail:
Ellis Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Post-Doctoral Research Associate
✨Tip Number 1
Network with current and former members of the Edinburgh NLP Group. Engaging with them can provide insights into the group's culture and ongoing projects, which can help you tailor your approach during interviews.
✨Tip Number 2
Familiarise yourself with the latest research published by the Edinburgh NLP Group and related conferences. Being well-versed in their work will not only impress during discussions but also demonstrate your genuine interest in their research focus.
✨Tip Number 3
Prepare to discuss your previous research experiences in detail, especially those that align with Natural Language Processing and Machine Learning. Highlight specific challenges you faced and how you overcame them, showcasing your problem-solving skills.
✨Tip Number 4
Practice explaining complex concepts in simple terms. Since the role requires clear communication, being able to articulate your research and its implications effectively will set you apart from other candidates.
We think you need these skills to ace Post-Doctoral Research Associate
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your PhD and any relevant research experience in Natural Language Processing and Machine Learning. Include specific projects you've worked on, especially those that demonstrate your ability to improve robustness and explainability in models.
Craft a Strong Cover Letter: In your cover letter, express your enthusiasm for the position and the Edinburgh NLP Group. Discuss how your skills align with the job requirements, particularly your experience with independent research and interdisciplinary collaboration.
Highlight Publications: List your high-quality publications in top-tier ML and NLP venues prominently in your application. This will showcase your research performance and contributions to the field, which are crucial for this role.
Demonstrate Communication Skills: Since the role requires clear communication of complex information, include examples in your application where you've successfully communicated research findings, whether in writing or presentations. This could be through conference talks, workshops, or published papers.
How to prepare for a job interview at Ellis
✨Showcase Your Research Experience
Be prepared to discuss your previous research projects in detail. Highlight your contributions, the methodologies you used, and any challenges you overcame. This will demonstrate your ability to conduct independent research and work effectively within a team.
✨Familiarise Yourself with Current Trends
Stay updated on the latest advancements in Natural Language Processing and Machine Learning. Be ready to discuss recent papers or breakthroughs in the field, especially those related to robustness and explainability of Large Language Models, as this aligns closely with the role.
✨Demonstrate Your Programming Skills
Prepare to showcase your programming abilities, particularly in Python and relevant libraries like PyTorch or TensorFlow. You might be asked to solve a coding problem or discuss how you've applied these skills in your research.
✨Communicate Clearly and Confidently
Practice explaining complex concepts in a clear and concise manner. During the interview, focus on articulating your thoughts logically and confidently, as effective communication is key in collaborative research environments.