Associate researcher with expertise in the field of machine learning

Description of the workplace

InfraVis is Sweden’s national infrastructure for visualization of scientific data. Currently, InfraVis employs over 50 experts across nine universities. CIPA is Lund University’s local infrastructure for image processing and analysis. CIPA is also the coordinating unit for Lund University's InfraVis node, consisting of around 10 people with complementary expertise.

InfraVis and CIPA provide all employees with professional development opportunities and strive to meet each individual’s development and well-being goals as much as possible. As an associate researcher with expertise in the field of machine learning within InfraVis/CIPA, you will have access to both local and national colleagues for stimulating exchanges, discussions, and joint efforts to solve complex challenges, develop new methods, and support research in a wide range of scientific domains nationwide through visualization expertise.

We offer

Lund University is a governmental authority, which means you get special benefits, generous holidays and a favourable occupational pension. We also have a flexible time agreement that creates good conditions for a balance between work and leisure.

Read more on the university's website about being employed at Lund University, Work with us.

Work duties and responsibilities

The primary task is to contribute as an expert in various research projects supported by CIPA/InfraVis. Regular and active participation in joint activities, including meetings, is required to promote knowledge transfer and infrastructure development. The position includes opportunities for career and skill development. Teaching and administrative tasks may constitute up to 20% of the work.

The work will focus on the development and application of advanced machine learning methods, and may involve the following areas:

• Identification of missing, noisy, or erroneous data.
• Data cleaning and generation. Natural Language Processing (NLP).
• Development of enhanced loss functions and information-theoretic methods for optimized data analysis.
• Machine learning-based segmentation of tomographic data (e.g., synchrotron X-ray microtomography).
• Visualization techniques, including the use of VR (Virtual Reality) and AR (Augmented Reality).
• Design and use of autoencoders (VAEs, GANs), diffusion models, and other ML methods for analyzing and discovering patterns in probability distributions in latent space.

Qualifications

We seek an experienced and driven individual who collaborates well and thrives in a complex research environment.

Requirements for the position are:

• Ph.D. or equivalent experience in a relevant subject.
• Excellent oral and written English skills.
• Publications in machine learning.
• Experience in image processing and analysis of tomographic or other imaging data.
• High motivation, ability to quickly learn new techniques, and strong time management skills.
• Strong collaboration and communication skills.

Meritorious for the position are:

• Experience in Natural Language Processing.
• Experience in maintaining computers and software.
• Proven ability to collaborate with researchers from different disciplines, industry, and policymakers.
• Experience with autoencoders or transformers.

Consideration will also be given to how the applicant's experience and expertise complement and strengthen the ongoing activities within the infrastructure and how they can contribute to its future development.

Terms 

The position is permanent,100%. Desired start date is 2025-04-01 or as agreed. Probationary period of 6 months will be applied. 

How to apply 

Apply through the university’s recruitment system. The application should include:

A cover letter explaining your motivation and how the position aligns with your qualifications, a CV. Degree certificates or equivalent. Contact details for at least two references. Any other supporting documents (e.g., reference letters). 

The university applies individual salary setting. Please indicate your salary requirements in your application. 

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