Research Assistant - Computer Vision and Medical Imaging (m/f/d)

The Image Data Exploration and Analysis Lab () at the Department Artificial Intelligence in Biomedical Engineering (AIBE) has an exciting opening to be filled as soon as possible for

Join the Cutting-Edge World of Deep Learning for Medical Image Analysis!

The Image Data Exploration and Analysis (IDEA) led by Prof. Bernhard Kainz, is seeking highly motivated and talented researchers in the field of Deep Learning for Medical Image Analysis. This is a full-time, fixed-term position, with secure options for future contract extensions. We support further scientific qualification (e.g., PhD or Habilitation) and offer an inspiring environment where your research can thrive.

This position offers the chance to work with real-world healthcare data to derive disease state labels using advanced data-driven methods. You'll tackle exciting challenges like self-supervised learning, leveraging category theory and symmetry-exploiting methods for noisy and incomplete labels, domain adaptation, and human-in-the-loop approaches. Your work will directly contribute to advancing medical diagnosis, treatment, and healthcare systems.

What We Offer

• A vibrant research environment within an internationally renowned lab.
• The opportunity to publish in leading venues and journals like MICCAI, CVPR, ECCV, IEEE TMI, ICLR, ICML, NeurIPS, Nature, and Medical Image Analysis.
• Collaboration with world-class partners at institutions like Stanford, MIT, Imperial College London, and NYU, with potential for extended research stays.
• A competitive salary aligned with TV-L regulations ( 55,000/year).
• Additional benefits including operational pension schemes, flexible working hours, remote work options, and excellent family-work compatibility.

Key Responsibilities

• Conduct world-class research, presenting and publishing results at top-tier conferences and journals.
• Active contribution to the lab s research mission, including collaboration with clinical, industrial, and academic partners.
• Teaching responsibilities, including up to 5 SWS (semester weekly hours), depending on the position's co-funding.
• Support in supervising BSc, MSc, and PhD projects.

Notwendige Qualifikationen:

Essential Qualifications

• A Master's or PhD in Computer Science, Mathematics, or a related field.
• Strong foundation in machine learning, computer vision, or medical image analysis.
• Proven programming skills in Python (plus tools like R, Unix shell scripting, etc.), demonstrated through past projects or public repositories.
• Excellent communication, English language skills, and a structured, independent work style.

Wünschenswerte Qualifikationen:

Desirable Qualifications

• A strong publication track record for your career stage, including papers in MICCAI, CVPR, ECCV, or prestigious Q1 journals like IEEE TMI and Medical Image Analysis.
• Experience in teaching, research organization, or industrial collaboration.
• Familiarity with self-supervised learning, category theory, or human-in-the-loop systems is a bonus!

Why FAU?FAU is a progressive, inclusive employer in a city with an excellent quality of life, short commutes, and a thriving academic and cultural community. Our lab has won international awards and prizes, and we pride ourselves on pushing boundaries in medical deep learning. You'll join a team that values curiosity, collaboration, and cutting-edge innovation.

Befristetes Forschungsvorhaben

The final job classification within TV-L is subject to conditions of Bavarian and German public service regulations (approx. 55,0000 salary per year).

Interested? Please send:

• A cover letter outlining your motivation and fit for the role.
• A CV, including your publication list.
• A 2-page research statement describing your vision for future work.
• Copies of certificates and contact details for at least one reference.
• Email your application as a single PDF file to by 28.02.2025. If you have questions, feel free to contact Prof. Bernhard Kainz directly.

Commitment to DiversityFAU is committed to equality, diversity, and inclusion. Applications from underrepresented groups, including women, minorities, and individuals with disabilities, are highly encouraged. Preferential consideration will be given to candidates with equal qualifications who are severely disabled. Part-time options may be available unless stated otherwise.