Summary
The University of Cambridge is offering a prestigious scholarship for the position of Research Assistant/Associate in Machine Learning. This interdisciplinary role focuses on machine-learning-guided antibiotic discovery, aimed at developing predictive models for the design of new antibiotics targeting Klebsiella pneumoniae.
Research Assistant/Associate in Machine Learning, Department of Engineering, Central Cambridge, UK
Designation
- Position: Research Assistant/Associate in Machine Learning
Key Details
| Category | Details |
|---|---|
| Research Area | Machine Learning, Antibiotic Discovery |
| Location | Department of Engineering, Central Cambridge |
| Eligibility/Qualification | PhD in Computer Science, Information Engineering, Statistics, Chemistry, Biology, or related area; strong publication record; excellent mathematical and programming skills |
| Salary Range | Research Assistant: GBP 33,002 – GBP 35,608; Research Associate: GBP 37,694 – GBP 46,049 |
| Contract Duration | Fixed-term for 24 months |
Job Description
The successful candidate will work under the supervision of Professors José Miguel Hernández Lobato and Andres Floto. Responsibilities include:
- Development of predictive and generative machine learning methods.
- Translating experimental measurements into scalable computational models.
- Engaging in deep learning, deep generative modeling, and molecular design.
- Assisting with research proposals, presentations, and publications.
- Networking and collaborating with colleagues and students.
- Planning and organizing research resources and workshops.
How to Apply
Interested candidates should:
- Click on the ‘Apply’ button on the official recruitment page.
- Upload a Curriculum Vitae (CV), a covering letter, and a publication list in the specified section.
- Ensure that referees can submit their letters before the interview date.
Last Date for Apply
- Closing Date: 15 May 2026
For more information or inquiries, please contact Kimberly Cole at div-f@eng.cam.ac.uk, and reference NM49458 in any correspondence.
This scholarship opportunity represents a significant chance for individuals looking to contribute to cutting-edge research in antibiotic discovery through machine learning.
