PhD Researcher in Ultrasound-Based Wall Shear Stress Estimation
2 weeks ago
Transform Medical Science with Us
Danmarks Tekniske Universitet (DTU) is a leading technical university recognized globally for its research, education, and innovation. We are seeking an ambitious PhD candidate to contribute to our groundbreaking research in medical imaging and machine learning.
Join our team of experts in developing an innovative high-frame-rate ultrasound-based wall shear stress (WSS) estimation framework. This project aims to transform stroke prevention by integrating state-of-the-art machine learning techniques with advanced ultrasound imaging.
About the Project
The project focuses on developing a novel WSS estimation framework by integrating high-frame-rate ultrasound with advanced deep learning algorithms. This will enable accurate measurements and address key research challenges, including robust vessel wall segmentation and flow estimation using physics-informed neural networks.
What You Will Achieve
- Cutting-Edge Research Experience: Engage in pioneering research at the intersection of medical imaging and machine learning, contributing to advancements in stroke prevention and clinical workflows.
- Advanced Skill Development: Develop and refine your expertise in high-frame-rate ultrasound imaging, machine learning, and AI-based image analysis, positioning yourself at the forefront of technological innovation.
- Professional Growth: Benefit from mentorship by leading experts in the field, access state-of-the-art facilities, and participate in professional development opportunities through courses, workshops, and conferences.
Responsibilities and Qualifications
We are seeking a motivated candidate with experience in deep learning for image processing and analysis. Your main focus will be to enhance the department's expertise in ultrasound flow imaging and deep learning, contributing to the development of innovative diagnostic tools.
Your primary tasks will be to:
- Conduct research in the field of high-frame-rate ultrasound imaging and machine learning.
- Develop and implement machine learning models for vessel wall segmentation and WSS estimation.
- Test and refine the proposed techniques through in-vitro flow phantom studies.
- Collaborate with clinical partners at hospitals for data collection and clinical validation.
- Publish research findings in international peer-reviewed journals and present at conferences.
- Teach and supervise BSc and MSc student projects.
Key Professional Competencies Needed
- Technical Expertise: Strong background in applied mathematics, computer science, biomedical engineering, or related fields; proficiency in image processing, computer vision, and machine learning techniques.
- Programming Skills: Advanced programming skills in languages such as Python, MATLAB, or similar, with experience in relevant deep learning packages.
- Medical Imaging Knowledge: Familiarity with medical imaging modalities, particularly ultrasound imaging; experience in high-frame-rate ultrasound imaging is highly desirable.
- Analytical Skills: Ability to analyze complex and large datasets, develop new methodologies, and validate the accuracy of the developed models.
Application Procedure
Your complete online application must be submitted no later than 4 September 2024 (23:59 Danish time). Applications must be submitted as one PDF file containing all materials to be given consideration.
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