Research Associate in Machine Learning and Computer Vision

Role Description

A position for a Postdoctoral Research Associate with expertise in Machine Learning and Computer Vision is now open at University of York to work on a project on continual learning. The project is a collaboration between Dr. Elena Geangu’s lab (Psychology Department, University of York) and Google DeepMind research scientists (London), which brings together expertise in computer science and developmental psychology for making significant contributions to understanding mechanisms of learning. For this project we will benefit from an unique multimodal data set (EgoActive) comprising thousands of hours of egocentric audio-video recordings and physiological signals captured from infants and adults. The project findings will have implications both for advances in machine learning and human mind development, with the ultimate goal that the two areas of research mutually inform each other.

The position is fully funded for 12-months, and funds to cover participation at scientific events will be provided. Additionally, the project will benefit from a significant compute allocation in Google Cloud.

This role is an excellent opportunity for contributing to groundbreaking research, building expertise in interdisciplinary research, and gaining experience collaborating with world leading industry partners.

Department

The Department of Psychology at University of York is internationally recognised for conducting some of the UK's most groundbreaking research in experimental psychology. Its researchers are supported by excellent research facilities which allow the use of cutting-edge experimental methods across the entire lifespan. Importantly, the Department of Psychology at the University of York is a hub of interdisciplinary innovation, leading projects that bring together many disciplines and areas of expertise for understanding how the human mind works and develops.

Role

We are looking for someone who can train large-scale models in a self-supervised regime, using data sampled from the infant and parent streams, and test the models’ performance in order to understand the benefits of a developmental curriculum for learning and the properties of the data that drive the performance. The successful candidate will also research aspects related to surprise and curiosity as they occur in human infant and machine learning.

The role holder will have an excellent command of spoken and written English. 

Key duties and responsibilities will include:

• To design and conduct research that: 1) leverages the multimodal data collected with infants and their parents to train large-scale models in a self-supervised regime, using data sampled from the infant and parent streams. Study various algorithms and analyse performance to understand the benefits of the developmental curriculum for learning and the properties of the data that drive the performance; 2) capitalizes on the unique multimodal data (namely the ECG and acceleration data synchronised with video and audio data), and the interdisciplinary expertise in machine learning and infant development, to study aspects related to surprise and curiosity as they occur in infant learning, as well as in self-supervised cross-modal learning.
• Preprocess all data modalities and structure in a format (e.g. tfrecords) that can be used to efficiently run machine learning experiments; pseudo-label the data using pre-trained models; design suitable evaluations and visualization techniques to assess model performance qualitatively and quantitatively; interpret research results relative to existent empirical data and theoretical proposals; use of appropriate research techniques and methods; write up of research results for dissemination through publications,  seminar and conference presentations; contribute to the identification of possible new ideas for research.
• To undertake appropriate organisational and administrative activities connected to the research project, including those related to respecting the data protection legislation.
• To assist in disseminating and promoting best practices for interdisciplinary research.      
• To disseminate research results from the project through high-quality journal articles and presentations at national and international conferences, and workshops.

Please see the job description for more information on key duties and responsibilities, as well as skills, experience, and qualifications needed for this position.

Interview date: To be confirmed

Start date: the start date of the position is January/February 2026 or sooner.

For informal enquiries: please contact Dr. Elena Geangu via email (elena.geangu@york.ac.uk).

The University strives to be diverse and inclusive  – a place where we can ALL be ourselves.

We particularly encourage applications from people who identify as Black, Asian or from a Minority Ethnic background, who are underrepresented at the University. 

We offer family friendly, flexible working arrangements, with forums and inclusive facilities to support our staff. #EqualityatYork