Machine Learning Research Engineer

The Job

We are looking for a highly motivated Research Engineer to join our interdisciplinary group in developing infrastructure for research on deep learning for medical image analysis. The Research Engineer will support ongoing research and development of machine learning methods for medical imaging applications, such as ML for accelerated MRI [1, 2, 3], breast cancer detection [4, 5, 6] and musculoskeletal [7] and brain image [8, 9, 10] analysis.

Required Qualifications

• Passion for engineering and research
• Dedication and attention to detail
• Ability to work in large interdisciplinary teams
• BS in computer science, mathematics, physics, electrical engineering or related discipline (MS of PhD is a plus)
• Expert skills in Python; skills in PyTorch or Tensorflow are a plus
• Good skills in using Linux and tools such as Git and Docker
• Practical basic knowledge of machine learning; Advanced knowledge of machine learning—especially deep learning—is a plus
• Experience working with medical imaging data is a plus

Responsibilities Will Include

• Extraction and curation of imaging data set across different applications
• Implementation of machine learning training and validation pipelines
• Implementation of baseline deep learning models
• Building novel deep learning models tailored to medical image analysis

To Apply

Send your CV with a short motivation letter to Yvonne Lui, MD, at yvonne.lui@nyulangone.org and to Krzysztof Geras, PhD, at k.j.geras@nyu.edu by March 31, 2021. In the subject of the email write:

[machine learning research engineer 2021]

Our Center, located in midtown Manhattan, is operated by the research arm of the radiology department of NYU Langone Health. The research division comprises approximately 130 full-­time personnel dedicated to imaging research, development, and clinical translation.

We focus on development of novel methods for rapid continuous comprehensive imaging, including but not limited to deep-learning image reconstruction and interpretation techniques, novel rapid MR acquisition and reconstruction strategies, innovative RF detectors and transmitters, new quantitative biomarkers for MRI and PET, and emerging MRI-based models of tissue microstructure.

We are a highly collaborative group and work in interdisciplinary, matrixed teams that include engineers, scientists, clinicians, technologists, and industry experts. We encourage collaboration across research groups to promote creativity and nurture an environment conducive to breakthrough innovations at the forefront of biomedical research.

Learn more about our Center.

Center for Advanced Imaging Innovation and Research (CAI²R) is operated by the department of radiology at NYU Langone Health. This arrangement brings together a vast amount of human and technological resources in basic magnetic-resonance science (physics, engineering, mathematics) and clinical applications (radiology, medicine, neurology, etc.). Our machine learning researchers benefit from access to massive datasets and computational clusters with more then 300 leading-edge GPUs.

Learn more about our environment, facilities, and instrumentation.

Apply no later than March 31, 2021. We expect the hired candidate to start during the summer or fall of 2021. The initial hire will be for one year, with an intention to renew, depending on mutual agreement. We offer a competitive salary and benefits package. Domestic and international candidates are welcome to apply.

We are committed to diversity and inclusion in all aspects of recruiting 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.

[1] K. Hammernik et al., Learning a variational network for reconstruction of accelerated MRI data. MRM, 2018.
[2] F. Knoll et al., Assessment of the generalization of learned image reconstruction and the potential for transfer learning. MRM, 2019.
[3] J. Zbontar et al., fastMRI: An Open Dataset and Benchmarks for Accelerated MRI. 2018.
[4] N. Wu et al., Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening. IEEE TMI, 2019.
[5] Y. Shen et al., Globally-Aware Multiple Instance Classifier for Breast Cancer Screening. MLMI, 2019.
[6] N. Wu et al., Breast density classification with deep convolutional neural networks. ICASSP, 2018.
[7] C.M. Deniz et al., Segmentation of the proximal femur from MR images using deep convolutional neural networks. Scientific Reports, 2018.
[8] S. Liu et al., On the design of convolutional neural networks for automatic detection of Alzheimer's disease. 2019.
[9] A. Kaku et al., DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation. 2019.
[10] Y. wang et al., Generalized Recurrent Neural Network accommodating Dynamic Causal Modeling for functional MRI analysis. Neuroimage, 2018.