Research Associate or Research Fellow In Machine Learning (2 Posts Available) Job at Manchester Full Time

We are seeking motivated and collaborative individuals to join our team as Postdoctoral Research Associate or Research Fellow in Machine Learning. These roles offer an exciting opportunity to contribute to internationally leading research within the School of Engineering, working in the Centre for AI Fundamentals. You will join Professor Samuel Kaski’s research group, collaborating across the University of Manchester, the ELLIS Institute Finland, the Turing Institute and industry partners, within a dynamic and inclusive research environment.

Two fixed‑term posts are available: one for two years and one for one year, working full time (35 hours per week).

You will be responsible for:

• Developing high‑quality research in machine learning, including probabilistic modelling and inference
• Contributing to new principles of AI assistance and collaborative AI
• Applying research methods to real‑world use cases with academic and industry collaborators
• Publishing research findings in leading journals and conferences and presenting work to diverse audiences
• Working collaboratively within an interdisciplinary, international research team

We welcome candidates who bring diverse perspectives, experiences, and approaches to their work.

About You
We encourage applications from individuals with a wide range of backgrounds and experiences. You should demonstrate:

Essential Criteria

• A PhD (or close to completion) in Machine Learning, Computer Science, Statistics or a closely related discipline
• Strong research expertise in machine learning or probabilistic modelling
• Evidence of high‑quality research outputs, such as publications in relevant venues
• Ability to work independently and collaboratively as part of a research team
• Excellent communication and organisational skills

Desirable Criteria

• Experience in areas such as collaborative AI, multimodal models, uncertainty‑aware learning or out‑of‑distribution methods
• Experience working on interdisciplinary or industry‑linked research projects
• Experience presenting research at international conferences
• Experience contributing to grant‑funded research or supervising students

We value transferable skills, creativity and real‑world research experience as much as formal qualifications.

Our benefits include:

• Generous employer pension contribution
• 29 days annual leave plus bank holidays, along with Christmas closure
• Access to world‑class research facilities and international collaboration opportunities

For more information, please see www.manchester.ac.uk/connect/jobs/benefits-working-here. You can also find information on our Flexible and Hybrid working here: www.manchester.ac.uk/connect/jobs/flexible-working.

We are an open place of enquiry and challenge. We embrace and celebrate difference, diversity and debate, and we pride ourselves on being a place of education, learning and community. Find out more from our Freedom of Speech Policy: www.staffnet.manchester.ac.uk/news/display/?id=32905.

Enquiries about the role, shortlisting and interviews
Name: Barbara Ruggeri
Email Address: ai-fun@manchester.ac.uk

General enquiries and administrative support
recruitmentservices.people@manchester.ac.uk

Technical and job portal support
jobseekersupport.jobtrain.co.uk/support/home

Applications close at midnight on the closing date.