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<div><span>The Center for Biomedical Imaging and the Center for Advanced Imaging Innovation & Research (CAI</span><span><span>2</span></span><span>R)
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.</span></div><div><span><br></span></div><div><b>Requirements include:</b></div><ul><li>Passion for engineering and research.</li><li>Dedication and attention to detail.</li><li>Ability to work in large interdisciplinary teams.</li><li>BS in computer science, mathematics, physics, electrical engineering or a related discipline. MS or PhD is a plus.</li><li>Expert skills in Python. Skills in PyTorch or Tensorflow are a plus.</li><li>Good skills in using Linux and tools such as git and Docker.</li><li>Practical basic knowledge of machine learning. Advanced knowledge of machine<br>learning, especially deep learning, is a plus.</li><li>Experience in working with medical imaging data is a plus.</li></ul><b>Responsibilities will include:</b><br><div><ul><li>Extraction and curation of imaging data set across different applications. </li><li>Implementation of machine learning training and validation pipelines.</li><li>Implementation of baseline deep learning models.</li><li>Building novel deep learning models tailored to medical image analysis.<br></li></ul><b>Timeline, Salary, and Benefits</b><br></div><div>Please apply no later than 3/31/2021. <br>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.</div><div><br></div><div><b>To Apply</b></div><div>Please send your application (CV and a short motivation letter) to Yvonne Lui (<a href="mailto:Yvonne.Lui@nyulangone.org">yvonne.lui@nyulangone.org</a>) and Krzysztof Geras (<a href="mailto:k.j.geras@nyu.edu">k.j.geras@nyu.edu</a>). Please use the string “[machine learning research engineer 2021]” as the subject of the email.</div><div><br></div><div><b>About Us</b><br>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.<br></div><div><br></div><div>To learn more about our research center, visit <a href="https://cai2r.net/">https://cai2r.net</a></div><div><br><b>References</b><br>[1] <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.26977">Learning a variational network for reconstruction of accelerated MRI data</a>. K. Hammernik et al. MRM, 2018.<br>[2] <a href="https://doi.org/10.1002/mrm.27355">Assessment of the generalization of learned image reconstruction and the potential for transfer learning</a>. F. Knoll et al. MRM, 2019.<br>[3] <a href="https://arxiv.org/pdf/1811.08839.pdf">fastMRI: An Open Dataset and Benchmarks for Accelerated MRI</a>. J. Zbontar et al. 2018.<br>[4] <a href="https://github.com/nyukat/breast_cancer_classifier">Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening</a>. N. Wu et al. IEEE TMI, 2019.<br>[5] <a href="https://arxiv.org/pdf/1906.02846.pdf">Globally-Aware Multiple Instance Classifier for Breast Cancer Screening</a>. Y. Shen et al. MLMI, 2019.<br>[6] <a href="https://github.com/nyukat/breast_density_classifier">Breast density classification with deep convolutional neural networks</a>. N. Wu et al. ICASSP, 2018.<br>[7] <a href="https://www.nature.com/articles/s41598-018-34817-6">Segmentation of the proximal femur from MR images using deep convolutional neural networks</a>. C. M. Deniz et al. Scientific Reports, 2018.<br>[8] <a href="https://arxiv.org/abs/1911.03740">On the design of convolutional neural networks for automatic detection of Alzheimer's disease</a>. S. Liu et al. 2019.<br>[9] <a href="http://arxiv.org/abs/1911.05567">DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation</a>. A. Kaku et al. 2019.</div><div>[10] <a href="https://www.ncbi.nlm.nih.gov/pubmed/29782993">Generalized Recurrent Neural Network accommodating Dynamic Causal Modeling for functional MRI analysis</a>. Y. Wang et al. Neuroimage, 2018.</div>
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