Master's Thesis @IDEA Lab - Erlangen, Deutschland - Friedrich-Alexander-Universität Erlangen-Nürnberg

Beschreibung

The successful applicant will be part of the Image Data Exploration and Analysis (IDEA) lab @AIBE FAU, which is the research group of Prof. Bernhard Kainz. We have pioneered new methodological developments in the field of medical image analysis and machine learning as evident from several international awards, prizes, and best paper awards. The group is internationally renowned for its research in medical deep learning, which has been proven by publications at top conferences (MICCAI, CVPR, ECCV, AAAI) and in prestigious journals (IEEE TMI, Medical Image Analysis, npj Nature Digital Health, The Lancet, etc.).

Your Tasks

Overview:

Our thesis project aims to advance the field of medical imaging by combining neural implicit representation spaces from various modalities and disease stages. This initiative involves integrating models, specifically LungMask for lung segmentation, and developing and training specialised models for detailed lung tissue segmentation and analysis. The primary objective is to evaluate tissue characteristics at the intersections, such as distinguishing between healthy lung tissue and scarred areas, across different modalities. We aim to adopt an unsupervised approach for evaluating lung disease progression.

Key Responsibilities:

• Develop and integrate neural implicit representation models to enhance the accuracy of lung tissue segmentation and analysis.
• Collaborate closely with researchers at our lab and medical professionals at UKER Hospital to ensure the practical applicability of the developed models.
• Conduct comprehensive evaluations of tissue characteristics at the intersections identified by the integrated models.
• Leverage your expertise in machine learning, neural networks, and medical imaging to advance the project.
• Focus on creating unsupervised methods for evaluating lung disease progression.

Your Profile

Necessary qualifications:

• Profound knowledge in Computer Science, Medical Engineering or Electrical Engineering (or similar study programmes)
• Profound (practical) knowledge of the programming language Python
• Profound (practical) knowledge of Machine learning and AI (PyTorch preferred)
• High motivation and high interest in the described topic
• Organised and independent way of working with a focus on quality and accuracy
• Very good communication skills in English

Desirable qualifications:

• Experience with neural implicit representations
• Experience in JAX
• if available, please provide a list of your public GitHub/GitLab repositories in your CV
• if available, please add an employer's reference or credentials
• if possible, please add the transcripts of records from your FAU study programme

Additional Information

Befristetes Forschungsvorhaben

Applications with missing information will be considered incomplete and will not be processed. Kindly refrain from sending emails regarding application status, we will contact only the successful applicant for this position.

Attention: Please note that we cannot provide financial compensation for the duration of your thesis.

The Department AIBE and FAU see itself as a progressive and forward-thinking employer. We welcome your application regardless of your age, gender, cultural and social background, religion, belief, disability or sexual identity. Friedrich Alexander University promotes professional equality for minority groups and women. These groups are therefore expressly encouraged to apply. Severely disabled persons within the meaning of the Severely Disabled Persons Act will be given preferential consideration in the case of equal professional qualifications and personal suitability if the advertised position is suitable for severely disabled persons. At the applicant's request, the Equal Opportunity Officer may be called in for the interview without any disadvantage to the applicant.

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