Our Center invites applications for several postdoctoral fellowships open to qualified candidates interested in transforming MRI into a precise, pathology-specific scientific instrument.
Postdoctoral fellows will work closely with principal investigators Els Fieremans, PhD, and Dmitry Novikov, PhD to contribute to rethinking the whole MRI pipeline: from image reconstruction and artifact correction, to biophysical modeling of brain microstructure and model validation, to clinical translation of novel, early-stage pathology biomarkers.
Fellowships are open in the following long-term, NIH-funded projects:
- Noise removal during image reconstruction.
The postdoctoral fellow will work with the team to use random matrix theory to separate noise from the signal in multi-modal raw MR data (diffusion and fMRI), develop a reconstruction pipeline, and translate the method for presurgical planning. This fellowship requires skills in one of the following:- MR reconstruction (ICE programming a plus)
- fMRI or diffusion processing
- artifact correction
- fiber tracking
- Distinguishing among demyelination, axonal loss, and inflammation
The fellow will work with the team to use biophysical modeling to develop clinically feasible MRI acquisitions and validate early sensitive markers for neurodegenerative diseases. This fellowship requires skills in one of the following:- Monte Carlo simulations of diffusion
- q-space trajectories
- diffusion theory or parameter estimation
- machine learning
- Clinical translation of cellular-level markers of pathology
The fellow will work with the team to translate the quantitative MRI pipeline into clinical studies of neurodegeneration (multiple sclerosis, Alzheimer’s disease, traumatic brain injury, migraine, and other pathologies). This fellowship requires skills in one of the following:- experience with big datasets
- background in neurological disorders
The fellows will be part of a broader microstructure ecosystem at NYU Grossman School of Medicine, with collaborative opportunities for animal validation (with Gene Kim, PhD, and Jiangyang Zhang, PhD), sequence development (with Steven Baete, PhD), image analysis (with Jelle Veraart, PhD), and clinical translation (with Timothy Shepherd, MD, PhD, and Yvonne Lui, MD).
Expected Qualifications
- earned or expected PhD degree in a relevant area (e.g., neuroscience, physics, computer science, biomedical engineering or a related field)
- publication record that demonstrates at least one of the skills listed above
- drive, creativity, collaborative disposition, strong motivation to acquire new skills and to engage in interdisciplinary research
About Us
CAI2R (pronounced care) comprises approximately 150 full-time personnel dedicated to imaging research, development, and clinical translation. Our team is diverse and highly collaborative. We work in interdisciplinary, matrixed groups that include engineers, scientists, clinicians, technologists, and industry experts.
Joining us means becoming part of a diverse community that values cross-pollination of ideas, celebrates creativity, and nurtures an environment conducive to breakthrough innovations.
Learn more about our mission, our research, and our team.
Environment
The Center for Advanced Imaging Innovation and Research (CAI2R) is supported by the NIH and operated by the department of radiology at NYU Langone Health.
Learn more about our research facilities.
Salary and Benefits
Each fellowship is for two years and may be extended, depending on the fellow’s interests and performance. Compensation will be commensurate with qualifications. NYU Langone Health offers competitive salary and generous benefits.
We are committed to diversity and inclusion in all aspects of recruitment and employment. All qualified individuals are encouraged to apply and will receive consideration without regard to race, color, gender, gender identity or expression, sexual orientation, national origin, age, religion, creed, or disability.
To Apply
Email a cover letter, a statement of research interests, a CV with a list of publications, and contact information of three references (all in PDF format) to meso@diffusion-mri.com.