At a Glance
- Tasks: Record and analyse ant behaviour using cutting-edge AI techniques.
- Company: Join the University of Bristol, a leader in biological sciences and inclusivity.
- Benefits: Enjoy hybrid working options and a competitive salary pro rata.
- Other info: Part-time role from September 2025 to January 2026, perfect for balancing studies.
- Why this job: Contribute to groundbreaking research while developing your skills in a supportive environment.
- Qualifications: PhD in biological sciences with experience in social insects and machine learning required.
The predicted salary is between 34786 - 40202 € per year.
We are seeking a part-time Postdoctoral Senior Research Associate in Computational Ethology / AI for Behaviour to deliver POSEIDANT: POSE-based Identification and Detection of ANT nest behaviours, a BBSRC International Institutional Award led by Dr Nathalie Stroeymeyt (University of Bristol) in collaboration with Dr Talmo Pereira (Salk Institute, developer of SLEAP). The post runs 1 Sept 2025-31 Jan 2026 (Month 1: 35% FTE; Months 2-5: 25% FTE) and focuses on automating the detection of nest-building behaviour in ant colonies to understand how architecture mitigates epidemic risk.
Hybrid working is available: Coding and data analysis can be carried out from home; experiments need to be performed on campus.
What will you be doing?
As part of the role, you will (1) record and process high-resolution video of nest construction by individually-marked Lasius niger colonies, with and without exposure to the fungal pathogen Metarhizium brunneum ; (2) train deep-learning models for pose-tracking and behavioural-classification; (3) combine the outputs of the deep-learning models with the automated barcode-based tracking to consolidate individual identification; (4) extract building behaviours from the videos and compare experimental treatments; (5) curate and release open-source code, annotated datasets and pre-trained models (e.g., via GitHub), including concise user documentation/tutorials
You should apply if
You have a PhD in biological sciences and have prior experience of working in the field of social insects, social interactions and collective behaviour. Experience with automated behaviour tracking in ants (data collection and analysis), and quantitative data analysis methods such as machine learning and behavioural classification in ants is essential. You will be ideal for this position if you are well-organised, eager to learn new skills and enthusiastic about studying social insects.
Additional information
For informal enquiries please contact Dr Nathalie Stroeymeyt nathalie.stroeymeyt@bristol.ac.uk
Contract type: Fixed term with 5 months funding
Work pattern: Part-time
Grade: £43,482 - £50,253 per annum pro rata
Salary: I
School/Unit: School of Biological Sciences
This advert will close at 23:59 UK time on Thursday 21st August 2025
Our strategy and mission
We recently launched our strategy to 2030 tying together our mission, vision and values.
The University of Bristol aims to be a place where everyone feels able to be themselves and do their best in an inclusive working environment where all colleagues can thrive and reach their full potential. We want to attract, develop, and retain individuals with different experiences, backgrounds and perspectives - particularly people of colour, LGBT+ and disabled people - because diversity of people and ideas remains integral to our excellence as a global civic institution. #J-18808-Ljbffr
Senior Research Associate in Computational Ethology / AI for Behaviour in Bristol employer: University of Bristol Law School
The University of Bristol is an exceptional employer, offering a vibrant and inclusive work culture that fosters collaboration and innovation in the field of biological sciences. With a strong commitment to employee growth, the university provides opportunities for professional development and encourages diverse perspectives, making it an ideal environment for those passionate about research in computational ethology and AI. Located in a dynamic city, the university supports hybrid working arrangements, allowing for flexibility while engaging in meaningful research that contributes to understanding social insect behaviour.
Contact Detail:
University of Bristol Law School Recruiting Team
StudySmarter Expert Advice🤫
We think this is how you could land Senior Research Associate in Computational Ethology / AI for Behaviour in Bristol
✨Tip Number 1
Network with professionals in the field of computational ethology and AI for behaviour. Attend relevant conferences or workshops where you can meet researchers and practitioners, including those from the University of Bristol and Salk Institute. Building these connections can provide insights into the role and potentially lead to a recommendation.
✨Tip Number 2
Familiarise yourself with the specific technologies and methodologies mentioned in the job description, such as deep-learning models for pose-tracking and behavioural classification. Engaging in online courses or tutorials related to these topics can enhance your understanding and demonstrate your commitment to the role.
✨Tip Number 3
Reach out to Dr Nathalie Stroeymeyt for an informal chat about the position. This not only shows your enthusiasm but also gives you a chance to ask questions that could help you tailor your approach when applying. A personal connection can make a significant difference.
✨Tip Number 4
Stay updated on recent research and advancements in social insect behaviour and machine learning applications in biology. Being knowledgeable about current trends will allow you to speak confidently about your interests and how they align with the goals of the project during any discussions or interviews.
We think you need these skills to ace Senior Research Associate in Computational Ethology / AI for Behaviour in Bristol
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your PhD in biological sciences and any relevant experience with social insects, particularly focusing on automated behaviour tracking and quantitative data analysis methods. Use specific examples to demonstrate your skills.
Craft a Strong Cover Letter:In your cover letter, express your enthusiasm for the role and the project. Mention your eagerness to learn new skills and how your background aligns with the requirements of the position, especially in relation to deep-learning models and behavioural classification.
Showcase Relevant Projects:If you have worked on similar projects, such as those involving pose-tracking or behavioural analysis, be sure to include these in your application. Provide details about your contributions and the outcomes of these projects.
Follow Application Instructions:Carefully read the job posting for any specific application instructions. Ensure that you submit all required documents, including your CV and cover letter, before the deadline of 21st August 2025.
How to prepare for a job interview at University of Bristol Law School
✨Showcase Your Expertise in Computational Ethology
Make sure to highlight your PhD and any relevant experience in biological sciences, particularly with social insects. Be prepared to discuss specific projects where you've applied your knowledge of collective behaviour and automated tracking.
✨Demonstrate Your Technical Skills
Since the role involves coding and data analysis, be ready to talk about your experience with deep-learning models and behavioural classification. Bring examples of your previous work, especially any open-source contributions or projects on platforms like GitHub.
✨Prepare for Practical Questions
Expect questions that assess your problem-solving skills in real-world scenarios, such as how you would approach automating the detection of nest-building behaviours. Think through potential challenges and solutions related to the project.
✨Express Your Enthusiasm for Learning
The role requires a willingness to learn new skills, so convey your eagerness to expand your knowledge in areas like machine learning and data analysis. Share examples of how you've successfully adapted to new technologies or methodologies in the past.
