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).

HiWi Offers

 

We continuously hire HiWis and would be happy to hear from you—feel free to come and talk to us anytime!.