Research Engineer in Data Science

The Job

One of the core research topics of our department is the development of machine learning methods for medical imaging applications. We have a broad spectrum of ongoing projects involving machine learning including image reconstruction [1,2,3], diagnostic classification [4,5] and image segmentation [6]. Our research covers all steps from basic science developments to the translation into clinical practice. We have ongoing industrial collaborations with Facebook AI Research and Siemens Healthcare. We are looking for a highly motivated research engineer to join our interdisciplinary and international group.

Expected Qualifications

  • Passion for both engineering and research
  • BS in computer science, mathematics, physics, electrical engineering or a related discipline; MS or PhD is big plus
  • Good knowledge of the principles of machine learning
  • Expert skills in Python. Skills in Tensorflow or PyTorch are a plus
  • Experience in working with medical imaging data, in particular MRI data, is a plus

Responsibilites Include

  • Implementation of deep learning models
  • BS in computer science, mathematics, physics, electrical engineering or a related discipline; MS or PhD is big plus
  • Performing experiments on our GPU cluster consisting of 96 NVIDIA V100 GPUs
  • Acquisition and curation of medical image datasets


The position is available immediately (December 2018) and applications are accepted until the position is filled. The initial appointment will be for a year, with an option to renew further, depending on mutual agreement. We offer a competitive salary and benefits package.

To Apply

Please send your CV and a short motivational statement to Florian Knoll, Krzysztof Geras and Daniel K Sodickson.


  1. Learning a variational network for reconstruction of accelerated MRI data. Hammernik et al. Magnetic Resonance in Medicine, 2018.
  2. Assessment of the generalization of learned image reconstruction and the potential for transfer learning. Knoll et al. Magnetic Resonance in Medicine, 2019.
  3. fastMRI: An Open Dataset and Benchmarks for Accelerated MRI. Zbontar, et al. ArXiv 2018.
  4. High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks. Geras et al., ArXiv 2017.
  5. Breast Density Classification with Deep Convolutional Neural Networks. Wu et al., ICASSP, 2018.
  6. Segmentation of the proximal femur from MR images using deep convolutional neural networks. Deniz et al., Scientific Reports, 2018.


Philanthropic Support

We gratefully acknowledge generous support for radiology research at NYU Langone Health from:
• The Big George Foundation
• Raymond and Beverly Sackler
• Bernard and Irene Schwartz

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