Full time – 35 hours per week

Fixed term contract - 36 months

The opportunity:

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).

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:

• Large scale data-driven modelling of neural population activity
• Dynamical system identification based on neural population recordings
• Predictive processing and sensorimotor coupling
• Active inference and experimental design for closed-loop experimentation 
• 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:

• 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
• A strong background in machine learning, signal processing, and algorithm development
• Demonstrated experience in dynamical systems (state-space models)
• Proficiency in Python and deep learning frameworks (PyTorch, Tensorflow, or similar)
• Proven experience in software development (e.g. active GitHub profile with past projects, released PyPI packages)
• Effective communication and teamwork abilities, and the ability to communicate across disciplines.

Desirable:

• Experience working with electrophysiological or optogenetic recordings data, or any biological data
• A track record of successful interdisciplinary collaboration, evidenced by joint publications or projects that integrate computational modelling with biological data collection
• Knowledge of dynamical system identification techniques (e.g., Kalman filter learning, Dynamic mode decomposition)