Overview
This is an exciting opportunity for a postdoctoral position in Numerical Analysis at Lund University, specifically within the Division of Mathematics at the Faculty of Engineering. The role is designed for researchers interested in the intersection of numerical methods and machine learning, contributing to significant challenges in mathematics for artificial intelligence.
Background & Relevance
Numerical analysis plays a crucial role in various fields, particularly in machine learning, where optimization methods are essential for model training and performance. The integration of advanced numerical techniques with machine learning applications is increasingly important, as it can lead to more efficient algorithms and solutions to complex problems. This position aims to explore these synergies, which are vital for advancing AI technologies.
Key Details
- Position: Postdoctoral Fellow in Numerical Analysis
- Location: Lund University, Sweden
- Duration: Fixed-term employment for a maximum of 2 years
- Funding: Supported by the Wallenberg AI, Autonomous Systems and Software Program
- Application Instructions: Application Link
Eligibility & Participation
Candidates must hold a PhD or an equivalent international degree in a relevant field, completed within three years prior to the application deadline. The position is aimed at early-career researchers looking to develop their research skills and contribute to ongoing projects in the department.
Submission or Application Guidelines
Interested applicants should follow the instructions provided in the application link. The selection process will focus on research qualifications, potential, and collaborative skills, with an emphasis on how candidates can enhance the department’s research capabilities.
Additional Context / Real-World Relevance
The research conducted in this position will focus on developing and analyzing novel numerical methods tailored for optimization problems in machine learning. This includes exploring advanced time-stepping methods and rigorous error analyses, which are critical for improving algorithm efficiency and robustness in real-world applications. The work will also engage with stochastic partial differential equations, highlighting the interdisciplinary nature of modern AI research.
Conclusion
This postdoctoral position at Lund University presents a unique chance for researchers to engage in cutting-edge work at the intersection of numerical analysis and machine learning. Interested candidates are encouraged to apply and contribute to impactful research in the field of AI.
Category: PhD & Postdoc Positions
Tags: numerical analysis, machine learning, partial differential equations, AI, Lund University, postdoctoral, optimization, stochastic methods
