Postdoctoral Research Fellow in Advanced Low-Field MRI

Key Responsibilities:

  • Develop and optimize hardware components to maximize signal sensitivity.
  • Investigate multi-channel and resonance-based techniques for improved imaging.
  • Implement AI-driven signal enhancement strategies.
  • Collaborate in an interdisciplinary research environment.

Candidate Profile:

  • PhD in MRI or a related field with a focus on system innovation.
  • Solid expertise in B₀/gradient design, RF coils, data acquisition, and image reconstruction.
  • Highly independent and self-motivated, with a strong collaborative mindset.
  • Passionate about advancing medical imaging through innovation.

 

PhD Position: Pioneering the Future of PET/MRI in Medical Imaging

 

Key Responsibilities:

  • Characterize detector performance systematically.
  • Develop integration concepts for PET modules in MRI.
  • Investigate RF shielding strategies.
  • Design cooling and power systems.
  • Collaborate with workshops and manufacturers.

Candidate Profile:

  • Passion for mechanical design; CAD skills (SolidWorks).
  • Ability to work in multidisciplinary teams.
  • Experience in data analysis and machine learning is a plus.
  • Basic knowledge of electromagnetic fields is advantageous.

PhD Position: Generative AI & Surgical Intelligence for Automated 3D Planning

 

Key Responsibilities:

  • You will pioneer the application of State Space Models (SSMs/Mamba) to automate complex surgical planning, replacing computationally heavy Transformer architectures.
  • Design 3D State Space Architectures: Develop „Volumetric-SSM“ backbones (e.g., Mamba) to process highresolution 3D anatomy (CT/DVT) with linear complexity, capturing global relationships without the bottlenecks of patch-based sliding windows.
  • Self-Supervised Pre-training: Create SSL strategies to learn robust anatomical representations from unlabelled data, drastically reducing the need for manual annotations.
  • Robustness Analysis: Systematically benchmark SSM efficiency and OOD robustness against JEPAs and Generative models, specifically regarding severe anatomical deformations.
  • Semantic & Geometric Integration: Interface high-level states with Multimodal LLMs for a semantic „Safety Layer“ and implement geometric algorithms (osteotomy planes, collision analysis) to fully automate the planning process.

Candidate Profile:

  • Excellent (top 10%) Master’s degree in Computer Science, Physics, Engineering, or a related field with a strong focus on AI/ML.
  • Proficient in Python and Deep Learning frameworks (PyTorch). Experience with Self-Supervised Learning (SSL), Transformers, or modern architectures like Masked Autoencoders (MAE) / JEPA is highly desirable.
  • Strong understanding of computer vision, representation learning, and high-dimensional geometry.
  • Passion for solving medical challenges and ability to work in multidisciplinary teams (engineers, clinicians).

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