Overview
The ELLIS Institute in Finland and the Manchester Centre for AI Fundamentals are offering postdoctoral and research fellow positions in the field of probabilistic machine learning. This opportunity is significant for those interested in advancing their research in innovative AI methodologies and applications across various domains.
Background & Relevance
Probabilistic machine learning is a crucial area within artificial intelligence that focuses on developing models that can make predictions based on uncertain data. This field is particularly relevant as it enables the creation of robust AI systems capable of handling real-world complexities. The research conducted at the ELLIS Institute and the Manchester Centre aims to push the boundaries of current methodologies, particularly in multimodal foundation models and collaborative machine learning, which are essential for the future of AI applications.
Key Details
- Application Deadline: December 1, 2025
- Location: ELLIS Institute, Finland and Manchester Centre for AI Fundamentals, UK
- Research Topics:
- Multimodal foundation models
- Out-of-distribution deployable machine learning
- Collaborative machine learning
- Funding Sources:
- ERC
- UKRI Turing AI World-Leading Researcher Fellowship
- Elliot AI
- Direct Links:
- Manchester Call
- Helsinki Call
Eligibility & Participation
These positions are aimed at researchers with a strong background in machine learning and related fields. Candidates who are passionate about applying probabilistic methods to real-world challenges are encouraged to apply.
Submission or Application Guidelines
Interested candidates should follow the application process outlined in the respective links provided for the Manchester and Helsinki calls. Ensure that all required documents are prepared and submitted before the deadline.
Additional Context / Real-World Relevance
The research conducted within these positions is expected to contribute significantly to the understanding and development of advanced AI systems. By focusing on probabilistic approaches, the work aligns with current trends in AI that prioritize adaptability and robustness, making it highly relevant in various sectors, including healthcare, finance, and environmental science.
Conclusion
This is an excellent opportunity for researchers looking to advance their careers in a dynamic and impactful field. Interested individuals are encouraged to explore these positions further and consider applying to contribute to cutting-edge research in probabilistic machine learning.
Category: PhD & Postdoc Positions
Tags: probabilistic machine learning, deep learning, multimodal ai, collaborative machine learning, AI4Research, AI4Science, ellis institute, manchester centre for ai fundamentals, erc funding, ukri turing fellowship
