Connectionists: Data Science Research Engineer position at the NYU School of Medicine

[Krzysztof Jerzy Geras]

The Center for Biomedical Imaging and the Center for Advanced Imaging
Innovation & Research (CAI2R) at NYU Langone Health are looking for a
highly motivated Research Engineer to join our interdisciplinary group and
help us build data science infrastructure. The engineer will support
ongoing research and development of machine learning methods for medical
imaging applications, such as ML methods for accelerated MRI [1, 2, 3],
breast cancer detection [4, 5, 6] and musculoskeletal [7] and brain image
[8, 9, 10] analysis.

Requirements include:

- Passion for engineering and research.

- BS in computer science, mathematics, physics, electrical engineering or a
related discipline. MS or PhD is a plus.

- Expert skills in Python. Skills in Tensorflow or PyTorch are a plus.

- Good skills in using Linux.

- Practical basic knowledge of machine learning. Advanced knowledge of
machine learning is a plus.

- Experience in 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.


Timeline, Salary, and Benefits

Please apply no later than 2/29.

We expect the appointed candidate to start during the summer of 2020. The
initial appointment will be for a year, with an intention to renew further,
depending on mutual agreement. We offer a competitive salary and benefits
package. We welcome both domestic and international applicants.

To Apply

Please send your application (CV and a short motivation letter) to Yvonne
Lui (yvonne.lui at nyulangone.org <Yvonne.Lui at nyulangone.org>) and Krzysztof
Geras (k.j.geras at nyu.edu). Please use the string “[data science research
engineer]” as the subject of the email.

About Us

The Center for Advanced Imaging Innovation & Research (CAI2R), 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 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.

To learn more about our research center, visit https://cai2r.net

References

[1] Learning a variational network for reconstruction of accelerated MRI
data <https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.26977>. K.
Hammernik et al. MRM, 2018.

[2] Assessment of the generalization of learned image reconstruction and
the potential for transfer learning <https://doi.org/10.1002/mrm.27355>. F.
Knoll et al. MRM, 2019.

[3] fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
<https://arxiv.org/pdf/1811.08839.pdf>. J. Zbontar et al. 2018.

[4] Deep Neural Networks Improve Radiologists' Performance in Breast Cancer
Screening <https://github.com/nyukat/breast_cancer_classifier>. N. Wu et
al. IEEE TMI, 2019.

[5] Globally-Aware Multiple Instance Classifier for Breast Cancer Screening
<https://arxiv.org/pdf/1906.02846.pdf>. Y. Shen et al. MLMI, 2019.

[6] Breast density classification with deep convolutional neural networks
<https://github.com/nyukat/breast_density_classifier>. N. Wu et al. ICASSP,
2018.

[7] Segmentation of the proximal femur from MR images using deep
convolutional neural networks
<https://www.nature.com/articles/s41598-018-34817-6>. C. M. Deniz et al.
Scientific Reports, 2018.

[8] On the design of convolutional neural networks for automatic detection
of Alzheimer's disease <https://arxiv.org/abs/1911.03740>. S. Liu et al.
2019.

[9] DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation
<http://arxiv.org/abs/1911.05567>. A. Kaku et al. 2019.

[10] Generalized Recurrent Neural Network accommodating Dynamic Causal
Modeling for functional MRI analysis
<https://www.ncbi.nlm.nih.gov/pubmed/29782993>. Y. Wang et al. Neuroimage,
2018.
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