Summary
The University of Cambridge is inviting applications for a highly motivated Research Assistant/Associate position in Machine Learning. This interdisciplinary project focuses on machine-learning-guided antibiotic discovery against the pathogen Klebsiella pneumoniae, integrating experimental measurements with cutting-edge AI approaches.
Research Assistant/Associate in Machine Learning (Fixed Term) for Antibiotic Discovery, Central Cambridge, USA
Designation
Research Assistant/Associate in Machine Learning
| Category | Details |
|---|---|
| Research Area | Machine Learning & Antibiotic Discovery |
| Location | Department of Engineering, Central Cambridge |
| Eligibility/Qualification | PhD (or close to obtaining) in Computer Science, Information Engineering, Statistics, Chemistry, Biology or related areas |
| Salary Ranges | Research Assistant: £33,002 – £35,608 Research Associate: £37,694 – £46,049 |
| Contract Duration | Fixed-term: 24 months |
| Reference | NM49458 |
Job Description
The successful candidate will:
- Collaborate under the joint supervision of Professor José Miguel Hernández Lobato and Professor Andres Floto.
- Work on deep learning, deep generative modeling, and molecular design within the Machine Learning Group.
- Develop predictive models and computational tools to support antibiotic design.
- Assist in research proposals, publications, teaching, and networking activities.
How to Apply
Interested applicants should:
- Register an account with the University of Cambridge recruitment system.
- Submit a Curriculum Vitae (CV), a covering letter, and a publication list online.
- Ensure that referees can submit their letters of recommendation before the interview period.
Last Date for Application
May 15, 2026
For any queries regarding the application or the vacancy, please contact Kimberly Cole at div-f@eng.cam.ac.uk. Please quote reference NM49458 in all correspondence.
Feel free to share this opportunity with interested candidates. The University actively supports equality, diversity, and inclusion in its hiring process.
