Overview
KTH Royal Institute of Technology in Stockholm, Sweden, is offering fully funded PhD and postdoc positions in the field of probabilistic machine learning. These roles are part of the Swedish AI research initiative known as WASP and aim to advance research in critical areas of machine learning.
Background & Relevance
Probabilistic machine learning is a vital area of study that addresses uncertainty in models, which is essential for the development of reliable AI systems. This field encompasses various methodologies, including Bayesian inference and active learning, which are crucial for enhancing the performance of machine learning models in real-world applications. As AI systems become more complex, understanding and quantifying uncertainty is increasingly important, making this research highly relevant to both academia and industry.
Key Details
- Positions Available: PhD student and Postdoc
- Location: KTH Royal Institute of Technology, Stockholm, Sweden
- Research Topics:
- Uncertainty quantification in large-scale models (e.g., LLMs, VLMs) and agentic AI
- Scalable Bayesian active learning (e.g., for AI for Science)
- Exact and approximate Bayesian inference with tractable models (e.g., probabilistic circuits)
- Application Links:
- PhD Position Call
- Postdoc Position Call
- Deadline for Applications: December 19th
- Starting Times: Flexible
Eligibility & Participation
These positions are targeted at individuals with a strong background in machine learning, statistics, or related fields. Candidates should be motivated to engage in cutting-edge research and contribute to a dynamic research environment at KTH.
Submission or Application Guidelines
Interested candidates should apply through the provided links for the PhD and postdoc positions. Ensure that all required documents are submitted by the deadline of December 19th.
Additional Context / Real-World Relevance
The research conducted at KTH will contribute significantly to the understanding and application of probabilistic models in AI. As AI continues to permeate various sectors, the ability to manage uncertainty and improve model reliability will be critical for the advancement of intelligent systems. This initiative aligns with global trends towards more robust and interpretable AI technologies.
Conclusion
This is an excellent opportunity for researchers looking to advance their careers in probabilistic machine learning. Interested individuals are encouraged to apply and become part of a vibrant research community at KTH, contributing to significant advancements in AI and machine learning.
Category: PhD & Postdoc Positions
Tags: probabilistic-machine-learning, kth, uncertainty-quantification, bayesian-learning, ai-research, machine-learning, sweden, active-learning, complex-systems
