Position Overview

This fully funded position focuses on exploring the connection between deep learning and kernel learning in the context of statistics and machine learning. The successful candidate will contribute to cutting-edge research aimed at understanding deep neural network performance and bridging the gap between classical statistical methods and modern machine learning techniques.

The project, funded by the Swedish Research Council, is supervised by Professor Rebecka Jörnsten as part of the initiative titled “Investigating Deep Learning through the Lens of Adaptive Kernels”. The research will explore:

1. How deep learning models learn and adapt,
2. How classical methods can achieve deep learning performance levels, and
3. Application of these methods for robust and interpretable modeling of large-scale biobank and cancer genomic data.

Your Responsibilities

• Conduct research on deep learning, adaptive kernels, and high-dimensional statistics.
• Develop and evaluate methods to connect classical kernel approaches with modern deep learning frameworks.
• Analyze and model large-scale biobank and genomic datasets to demonstrate the research outcomes.
• Publish scientific findings in peer-reviewed journals and present results at national and international conferences.
• Participate in departmental duties, including teaching and seminar activities, up to 20% of the position.
• Collaborate effectively within the Division of Applied Mathematics and Statistics.

Eligibility Requirements

To qualify for this position, you must:

• Hold a Master’s degree (or a 4-year Bachelor’s degree) in Mathematics, Applied Mathematics, Mathematical Statistics, or a related discipline. Degrees must be completed by September 1, 2025.
• Demonstrate strong analytical and problem-solving skills.
• Have experience in programming (e.g., Python, R, or similar languages).
• Show a solid foundation in statistics, machine learning, or related areas.

Meritorious qualifications:

• Knowledge of deep neural networks, kernel methods, or high-dimensional statistics.
• Experience with research programming or computational tools.
• Previous research experience in statistical or applied mathematics methods.

Language: Proficiency in English (oral and written) is required.

What We Offer

• A fully funded PhD position for up to 5 years, comprising 4 years of research and 1 year of departmental work.
• A stimulating and collaborative research environment at Sweden’s largest mathematics department.
• Supervision and mentorship by renowned faculty members, including Professor Rebecka Jörnsten.
• Opportunities to participate in international collaborations, conferences, and workshops.
• Access to cutting-edge computational resources and data.
• Employment benefits, including:
  • Flexible working conditions and a supportive research community.
  • Competitive salary based on the Swedish Higher Education Ordinance.
  • Parental leave policies and access to childcare facilities.
  • Employee wellness initiatives and workplace equality programs.

Application Process

Deadline: Applications must be submitted by February 20, 2025.

To apply, submit the following documents through the University of Gothenburg’s recruitment portal:

1. Application Letter: A maximum of 2 pages detailing your background, motivation, and research interests.
2. Curriculum Vitae: Including relevant academic achievements, technical skills, and publications (if any).
3. Academic Transcripts: Bachelor’s and Master’s transcripts.
4. Thesis Work: Include copies of your Bachelor’s and/or Master’s thesis. If not in English, provide a summary.
5. References: Names and contact information for at least two referees.