Career Opportunities

MIITT is seeking graduate students and/or postdoctoral research fellows to develop 3D motion-resolved Magnetic Resonance Fingerprinting (MRF) techniques, which will allow for simultaneous imaging of structure, function, and tissue properties (e.g., T1/T2 relaxation properties) over the entire heart from a rapid all-in-one scan. This work will build our existing 2D cine MRF implementation by: 1) extending this technique to 3D whole-heart imaging, 2) developing motion self-navigation techniques to allow the scan to be performed without breathholds or electrocardiogram gating. The goal of this project is to enable an objective and reproducible diagnosis of cardiomyopathy based on quantitative MRF data, which would allow patients to receive timely treatment that is targeted to a specific cardiomyopathy subtype. Helpful skills for this project include the ability to program MRI pulse sequences in Siemens IDEA, develop algorithms for detecting/correcting respiratory and cardiac motion in MRI data, develop MRI reconstruction algorithms, and program in MATLAB and/or Python. Promising candidates who do not yet possess these skills will be mentored and expected to learn them during the project. This work is in collaboration with University Hospitals Cleveland Medical Center (UHCMC), and the applicant will be expected to interface regularly with cardiologists. Please contact Jesse Hamilton for more details (hamiljes@med.umich.edu).

MIITT is seeking graduate students and/or postdoctoral research fellows to develop 3D high-resolution Magnetic Resonance Fingerprinting (MRF) techniques, which will enable a quantitative and automatic prostate cancer detection. This work will build our existing 2D and 3D MRF implementations by combining this technique with a multi-dimensional radio frequency encoding and spatial encoding strategies. Helpful skills would include the basic knowledge of MRI, the ability to perform MRI scans, optimize data acquisition trajectories, program pulse sequences in Siemens IDEA, develop and apply image reconstruction algorithms, and program in MATLAB and/or Python. Promising candidates who do not yet possess these skills will be mentored and expected to learn them during the project. The applicant will be expected to interface regularly with urologists and radiologists. Please contact Yun Jiang for more details (yunjiang@med.umich.edu).

MIITT is seeking graduate students and/or postdoctoral research fellows to develop novel acquisition and reconstruction solutions for low field cardiac MRI. The goal of this project is to develop rapid and user-friendly imaging techniques for evaluating heart structure, function, and tissue characteristics that can be deployed on low-cost 0.55T scanners, which has the potential to expand access to cardiac MRI worldwide. This project will involve the design of rapid data collection strategies tailored for low field scanners, and the development of AI-enabled image reconstruction methods to counter the inherently low signal-to-noise encountered at 0.55T. Current research areas include real-time cine imaging, phase contrast flow imaging, and relaxometry using MR Fingerprinting. Helpful skills include the ability to program MRI pulse sequences in Siemens IDEA; experience with deep learning and neural networks using TensorFlow, PyTorch, or a similar language; proficiency programming in MATLAB and/or Python; design optimized non-Cartesian MRI sampling trajectories; develop deep learning MRI reconstructions; and interface with cardiologists and radiologists. Promising candidates who do not yet possess these skills will be mentored and expected to learn them during the project. UM collaborators include Prachi Agarwal, MD and Nicholas Burris, MD. Please contact Jesse Hamilton for more details (hamiljes@med.umich.edu).

MIITT is seeking graduate students and/or post-doctoral researchers to explore novel breast MRI techniques. This project would build on our existing technologies including Magnetic Resonance Fingerprinting, but could move in directions including compressed sensing and/or machine learning depending on the interests of the candidate.  Helpful skills would include the ability to perform MRI scans, optimize data acquisition trajectories, program pulse sequences in IDEA, develop and apply image reconstruction algorithms, implement methods for platforms like BART and/or Gadgetron, and interface with radiologists.  Promising candidates who do not yet possess these skills will be mentored and expected to learn them during this project. Please contact Nicole Seiberlich for more details (nse@med.umich.edu).

MIITT is seeking undergraduates, graduate students and/or post-doctoral researchers to develop novel pulse sequences to provide unique tissue contrasts in MR images.  This primarily computational project would provide the researcher the ability to learn about MRI physics while developing new types of MRI data acquisition strategies. This project would build on our existing technologies using Magnetic Resonance Fingerprinting, but could move in directions including machine learning depending on the interests of the candidate.  Helpful skills would include the ability to program in Matlab and a desire to learn more about MRI pulse sequences. Promising candidates who do not yet possess these skills will be mentored and expected to learn them during this project. Collaborators would include Vikas Gulani and Graeme Bydder. Please contact Nicole Seiberlich for more details (nse@med.umich.edu).

MIITT is seeking graduate students and/or post-doctoral researchers to explore the application of MRI in the lung.  The goal of this project is to interface with lung researchers and pulmonologists to implement existing lung MRI techniques and develop methods where existing approaches may be lacking. Development will be performed in conjunction with other researchers at MIITT. While we anticipate that our 0.55T Free.Max system would be used for this project, we also have both a 1.5T and a 3T system available should the candidate seek to work at a higher field strength. Helpful skills would the ability to discuss lung physiology, interface with lung imaging researchers, radiologists, and pulmonologists, perform MRI scans in the lung, program pulse sequences in IDEA, and develop and apply image reconstruction algorithms. Promising candidates who do not yet possess these skills will be mentored and expected to learn them during this project.  UM collaborators would include Craig Galban. Please contact Nicole Seiberlich for more details (nse@med.umich.edu).

MIITT is seeking graduate students and/or post-doctoral researchers to develop novel pulse sequences and reconstruction algorithms to assess the effects of Magnetization Transfer on other quantitative measurements, and measure MT itself, in the brain and other organs.  This project would build on our existing technologies using Magnetic Resonance Fingerprinting, but could move in directions including compressed sensing and/or machine learning depending on the interests of the candidate.  Helpful skills would include the ability to model the effects of MT, perform MRI scans in the brain, optimize data acquisition trajectories, program pulse sequences in IDEA, and develop and apply image reconstruction algorithms. Promising candidates who do not yet possess these skills will be mentored and expected to learn them during this project. UM collaborators would include Scott Swanson; outside collaborators would include Shaihan Malik (KCL). Please contact Nicole Seiberlich for more details (nse@med.umich.edu).

MIITT is seeking graduate students, post-doctoral researchers, and/or clinical researchers to explore the use of our new 0.55T Free.Max MRI system.  Projects in this area are broad, ranging from the assessment of images from standard clinical protocols run on the 0.55T to the development of completely novel acquisition and reconstruction strategies on this magnet.  MIITT faculty are also open to other projects which specifically make use of lower field systems.  Helpful skills would the ability to work with technologists, radiologists, and our industrial partner (Siemens Healthineers). Please contact Nicole Seiberlich for more details (nse@med.umich.edu).

MIITT is seeking graduate students and/or post-doctoral researchers to develop novel pulse sequences and reconstruction algorithms for quantitative abdominal MRI.  The goal of this project is to develop, implement, and translate robust methods for 3D free-breathing abdominal imaging, with a focus on the collection of quantitative information.  This project would build on our existing technologies using Magnetic Resonance Fingerprinting and non-Cartesian parallel imaging approaches, but could move in directions including compressed sensing and/or machine learning depending on the interests of the candidate.  Helpful skills would include the ability to perform MRI scans, optimize data acquisition trajectories, program pulse sequences in IDEA, develop and apply image reconstruction algorithms, develop approaches for free-breathing 3D acquisitions using PilotTone and/or self-gating signals, and interface with abdominal radiologists. Promising candidates who do not yet possess these skills will be mentored and expected to learn them during this project. UM collaborators would include Vikas Gulani, MD, PhD and Matt Davenport, MD. Please contact Nicole Seiberlich for more details (nse@med.umich.edu).

MIITT is seeking graduate students and/or post-doctoral researchers to develop novel pulse sequences and reconstruction algorithms for real-time cardiac imaging.  This project would build on our existing technologies for real-time acquisitions and reconstructions based on non-Cartesian parallel imaging, but could move in directions including compressed sensing and/or machine learning depending on the interests of the candidate.  Helpful skills would include the ability to perform cardiac MRI scans, optimize data acquisition trajectories, program pulse sequences in IDEA, develop and apply image reconstruction algorithms, implement methods for platforms like BART and/or Gadgetron, and interface with cardiologists and radiologists.  Promising candidates who do not yet possess these skills will be mentored and expected to learn them during this project. UM collaborators would include Prachi Agarwal, MD; external collaborators include Cenk Cavusoglu, PhD and Mark Griswold, PhD (CWRU). Please contact Nicole Seiberlich for more details (nse@med.umich.edu).