Associate Researcher with expertise in machine learning - Hiring in process/Finished, not possible to apply

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 public 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 to various research projects within the framework of CIPA/InfraVis as an associate researcher with expertise in machine learning (ML). 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. 

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,
• Development of enhanced loss functions and information-theoretic methods for optimized data analysis,
• Machine learning-based image segmentation of tomographic data (e.g., synchrotron X-ray microtomography),
• 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:

• The position requires that you have a PhD in natural sciences/technology/medicine/mathematics/physics or equivalent, with a focus on machine learning,
• Excellent oral and written English skills,
• Publications in machine learning,
• Experience in image processing and image 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:

• Proven ability to collaborate with researchers from different disciplines, and industry,
• 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 a permanent position, 100% and a probationary period may apply. Desired start date is as soon as possible or as agreed.

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, and a CV. Degree certificates or equivalent. Contact details for at least two references. Any other supporting documents (e.g., reference letters).

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