Connectionists: Machine Learning Research Engineer position at 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 infrastructure for research on deep learning for medical
image analysis. 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.
   - Dedication and attention to detail.
   - Ability to work in large interdisciplinary teams.
   - BS in computer science, mathematics, physics, electrical engineering
   or a related discipline. MS or 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 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.
   - Building novel deep learning models tailored to medical image analysis.

*Timeline, Salary, and Benefits*
Please apply no later than 3/31/2021.
We expect the appointed candidate to start during the summer or fall 2021.
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 “[machine learning
research engineer 2021]” 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. We have access to datasets of massive sizes and
computational clusters with over 300 cutting edge GPUs.

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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