UE07, £41,064 - £48,822
School of Informatics
Full Time
Fixed term, 36 months
The Opportunity:
Your next step starts here. Be part of something bigger.
We offer careers and roles in just about every area you can think of. Whether you are looking for entry-level, mid-career or roles at the top of your chosen specialism, we can offer you a career where you can flourish and make a real difference in the communities around us while contributing to the world at large. Our people come from a wide range of backgrounds, each bringing their abilities and experiences to our organisation.
The University of Edinburgh is a world-class organisation. We look for the best in the field across all disciplines and provide a working environment where academics can develop their careers and passion for their chosen subject area. We offer the full range of academic roles and have a genuine focus on our students performance and wellbeing.
The research group of Dr. Nina Kudryashova, a Royal Society University Research Fellow, at the Institute for Machine Learning (IML) at the University of Edinburgh, invites applications for a Postdoc / Research Associate (36 months), to contribute to cutting-edge research in computational neuroscience, developing algorithmic foundations for closed-loop experimentation.
The post holder will be supervised by the principal investigator Dr. Nina Kudryashova. The group has established links to experimental neuroscience groups in Edinburgh (Duguid, Rochefort, Karnani) and Newcastle Hospitals NHS Foundation Trust (Bashford). The post holder will also have the opportunity to collaborate with colleagues in a wide range of specialties across the School, including in computational neuroscience (e.g. Hennig, Onken, Chadwick), computational cognitive neuroscience (e.g. Series, Peters), machine learning (e.g. Mac Aodha, Bilen, Sevilla-Lara, Vergari, Malkin, Borovitskiy), probabilistic programming (e.g. Narayanaswamy, Kammar, Belle), as well as in robotics and neurotechnology (e.g. Nazarpour, Webb).
The central purpose of the job is to investigate data-driven machine learning methods to enable the identification and control of neural population dynamics, providing actionable predictions for designing next-generation systems neuroscience experiments.
Your project will build on the group’s work on disentangling neural code for feedforward and feedback-driven control of movement [Kudryashova 2025, bioRxiv]. Our primary research aim is getting insights into closed-loop brain-environment interaction, particularly learning from behavioral perturbations or targeted neural stimulation. However, other research directions within NeuroAI will also be available for the post holder to explore. Being part of a newly established group, this post offers a unique chance to influence the trajectory of our group's work and contribute significantly to emerging NeuroAI research directions.
The post is ideal for researchers interested in the following areas:
1. Large scale data-driven modelling of neural population activity
2. Dynamical system identification based on neural population recordings
3. Predictive processing and sensorimotor coupling
4. Active inference and experimental design for closed-loop experimentation
5. Designing prototypes of software pipelines to embed models into closed-loop experimentation pipelines
The candidate is expected to take intellectual ownership of core scientific questions in this space, developing new ideas and driving collaborative projects towards significant publications, leveraging the expertise of the supervision team and other scientific collaborators. There are no formal teaching duties, allowing full flexibility for conducting research. There will be opportunities to mentor and work with PhD and MSc students working on related topics.
Your skills and attributes for success:
Essential:
6. A Ph.D. (or equivalent experience) in Machine Learning, Computer Science, Mathematics, Physics, Engineering, Computational Neuroscience, or a related field A track record of publications in top-tier journals (e.g. ELife or broad-interest science venues) and/or conferences in AI (e.g. NeurIPS, ICML, ICLR), or allied areas
7. A strong background in machine learning, signal processing, and algorithm development
8. Demonstrated experience in dynamical systems (state-space models)
9. Proficiency in Python and deep learning frameworks (PyTorch, Tensorflow, or similar)
10. Proven experience in software development (e.g. active GitHub profile with past projects, released PyPI packages)
11. Effective communication and teamwork abilities, and the ability to communicate across disciplines.
Desirable:
12. Experience working with electrophysiological or optogenetic recordings data, or any biological data
13. A track record of successful interdisciplinary collaboration, evidenced by joint publications or projects that integrate computational modelling with biological data collection
14. Knowledge of dynamical system identification techniques (e.g., Kalman filter learning, Dynamic mode decomposition)
People have always been at the heart of our work. As part of the University, you are a part of our community. We are looking for people with drive, determination, and a passion for what they do. We are a place where everyone is welcome and offer a range of policies and benefits designed to support you in building the right meaningful/personalised flexibility for you.
A career with us has a range of other benefits that can be tailored to your lifestyle:
15. Leading-edge research
16. Working within one of the world’s leading universities
17. Contributing to the work and purpose of the University.
This post is full-time (35 hours per week); however, we are open to considering flexible working patterns. We are also open to considering requests for hybrid working (on a non-contractual basis) that combines a mix of remote and regular on-campus working.
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