Overview
The University College London (UCL) is offering a funded PhD position in the LASP Lab, focusing on machine learning, optimization, and scientific modeling. This opportunity is particularly significant for those interested in advancing research in these interdisciplinary fields, supported by G-Research.
Background & Relevance
Machine learning and optimization are crucial areas within artificial intelligence, driving innovations across various sectors. The LASP Lab at UCL is known for its interdisciplinary approach, merging scientific modeling with advanced machine learning techniques. This PhD position aims to explore new methodologies in black-box optimization and generative models, which are essential for tackling complex decision-making problems, especially in sustainability contexts.
Key Details
- Duration: 4 years
- Funding: Fully funded by G-Research, covering tuition fees and providing a competitive stipend
- Supervisor: Dr. Laura Toni
- Research Areas:
- Black-box optimization and reinforcement learning for complex decision-making with a focus on sustainability.
- Generative models for graphs, including diffusion and connections to fluid dynamics.
- Application Deadline: 5 January 2026
- More Information: LASP Lab Website
- Application Guidance: UCL PhD Scholarship Details
Eligibility & Participation
This PhD position is aimed at candidates with a strong academic background in machine learning, optimization, mathematics, and statistics. Proficiency in Python is essential, along with a genuine interest in reinforcement learning, graph generative models, or autonomous agents.
Submission or Application Guidelines
Interested candidates should reach out to Dr. Laura Toni for further information. It is advised to personalize the email by briefly explaining how your background aligns with the focus areas of the project.
More Information
This PhD position not only offers a chance to contribute to cutting-edge research but also provides unique professional development opportunities, including mentorship from G-Research experts and participation in their “Spring into Quant Finance” program. Such experiences can significantly enhance a candidate’s career prospects in the rapidly evolving fields of AI and machine learning.
Conclusion
This is an excellent opportunity for motivated individuals looking to advance their careers in machine learning and optimization. Interested candidates are encouraged to apply and explore this unique chance to contribute to impactful research at UCL’s LASP Lab.
Category: PhD & Postdoc Positions
Tags: machine learning, optimization, reinforcement learning, graph generative models, ucl, autonomous agents, g-research, data science, quantitative finance, python, statistics, mathematics
