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Research Engineer in Data Science position at NYU School of Medicine

Country : USA - United States

Website : http://nyu.edu

Description

The mission of the Center for Advanced Imaging Innovation and Research (CAI2R) at NYU School of Medicine is to develop new medical imaging technologies and to deploy them for routine patient care. The center is embedded into the Department of Radiology and consists of over 80 full-time researchers. It is located in the center of Manhattan, adjacent to the UN headquarters.
Available Position
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.
Requirements include:
- 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.
Responsibilities include:
- Implementation of deep learning models.
- 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.
Contact
Please send your application (CV and a short motivational statement) to Florian Knoll (florian.knoll-AT-nyumc.org), Krzysztof Geras (k.j.geras-AT-nyu.edu) and Daniel K Sodickson (daniel.sodickson-AT-nyumc.org).
References
[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.

Last modified: 2018-12-29 22:01:59