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:

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)

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:

Leading-edge research
Working within one of the world’s leading universities
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.

View the full job description (opens in a new browser tab)

How to apply

Please include the following documents in your application:

- CV (max 2 pages)

- Cover letter (max 2 pages)

- A short research proposal (max 2 pages)

Contact details for informal enquiries: Nina Kudryashova nkudryas@ed.ac.uk

As a valued member of our team, you can expect:

A competitive salary.
An exciting, positive, creative, challenging and rewarding place to work.
To be part of a diverse and vibrant international community.
Comprehensive Staff Benefits, including generous annual leave entitlement, a defined benefits pension scheme, a wide range of staff discounts, family-friendly initiatives, and flexible work options. Check out the full list on our staff benefits page (opens in a new tab) and use our reward calculator to discover the value of your pay and benefits.

Championing equality, diversity, and inclusion

The University of Edinburgh holds a Silver Athena SWAN award in recognition of our commitment to advance gender equality in higher education. We are members of the Race Equality Charter, and we are also Stonewall Scotland Diversity Champions, actively promoting LGBT equality.

Prior to any employment commencing with the University, you will be required to evidence your right to work in the UK. Further information is available on our right to work webpages (opens new browser tab)

Key dates to note

Unless stated otherwise the closing time for applications is 11:59pm UK time. If you are applying outside the UK the closing time on our adverts automatically adjusts to your browsers local time zone.

As a world-leading research-intensive University, we are here to address tomorrow’s greatest challenges. Between now and 2030 we will do that with a values-led approach to teaching, research and innovation, and through the strength of our relationships, both locally and globally.

Informatics is the study of how natural and artificial systems store, process and communicate information. Research in Informatics promises to take information technology to a new level, and to place information at the heart of 21st century science, technology and society. The School enjoys collaborations across many disciplines in the University, spanning all three College, and also participates as a strategic partner in the Alan Turing Institute and is home to a number of Centres for Doctoral Training.

The School provides a fertile environment for a wide range of studies focused on understanding computation in both artificial and natural systems. It attracts students around the world to study in our undergraduate and postgraduate programmes. Informatics is one of seven schools in the College of Science and Engineering, at the University of Edinburgh. It is recognised for the employability of its graduates, its contributions to entrepreneurship, and the excellence of its research. Since the first Research Assessment Exercise in 1986, Informatics at Edinburgh has consistently been assessed to have more internationally excellent and world-class research than any other submission in Computer Science and Informatics. The latest REF 2021 results have again confirmed that ours is the largest concentration of internationally excellent research in the UK. This contributes to our ranking of consistently being in the top 30 world-wide.

We aim to ensure that our culture and systems support flexible and family-friendly working and recognise and value diversity across all our staff and students. The School has an active programme offering support and professional development for all staff; providing mentoring, training, and networking opportunities.

We are seeking a Research Associate at the Institute for Machine Learning (IML) in School of Informatics to contribute to cutting-edge research in computational neuroscience, developing algorithmic foundations for closed-loop experimentation.

Apply now

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