<div dir="ltr">

<p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">The Center for Biomedical Imaging and the Center for Advanced Imaging Innovation & Research (CAI</span><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"><span style="font-size:0.6em;vertical-align:super">2</span></span><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">R) 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.</span></p><br><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Requirements include:</span></p><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"></span><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Passion for engineering and research.</span></p><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- BS in computer science, mathematics, physics, electrical engineering or a related discipline. MS or PhD is a plus.</span></p><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Expert skills in Python. Skills in Tensorflow or PyTorch are a plus.</span></p><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Good skills in using Linux.</span></p><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Practical basic knowledge of machine learning. Advanced knowledge of machine learning is a plus.</span></p><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Experience in working with medical imaging data is a plus.</span></p><br><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Responsibilities will include:</span><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"><br></span></p><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Extraction and curation of imaging data set across different applications.</span></p><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Implementation of machine learning training and validation pipelines.</span></p><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">- Implementation of baseline deep learning models.</span></p><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"><br></span></p><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Timeline, Salary, and Benefits</span></p><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Please apply no later than 2/29. </span></p><br><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">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.</span></p><br><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">To Apply</span></p><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">Please send your application (CV and a short motivation letter) to Yvonne Lui (</span><a href="mailto:Yvonne.Lui@nyulangone.org" style="text-decoration:none" target="_blank"><span style="font-size:10pt;font-family:Arial;color:rgb(17,85,204);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:underline;vertical-align:baseline;white-space:pre-wrap">yvonne.lui@nyulangone.org</span></a><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">) and Krzysztof Geras (</span><a href="mailto:k.j.geras@nyu.edu" style="text-decoration:none" target="_blank"><span style="font-size:10pt;font-family:Arial;color:rgb(17,85,204);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:underline;vertical-align:baseline;white-space:pre-wrap">k.j.geras@nyu.edu</span></a><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">). Please use the string “[data science research engineer]” as the subject of the email.</span></p><br><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">About Us</span></p><p dir="ltr" style="line-height:1.2;text-align:justify;background-color:rgb(255,255,255);margin-top:0pt;margin-bottom:0pt;padding:0pt 0pt 15pt"><span style="font-size:10pt;font-family:Arial;color:rgb(51,51,51);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">The Center for Advanced Imaging Innovation & Research (CAI</span><span style="font-size:10pt;font-family:Arial;color:rgb(51,51,51);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap"><span style="font-size:0.6em;vertical-align:super">2</span></span><span style="font-size:10pt;font-family:Arial;color:rgb(51,51,51);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">R), 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.</span></p><p dir="ltr" style="line-height:1.2;text-align:justify;background-color:rgb(255,255,255);margin-top:0pt;margin-bottom:0pt;padding:0pt 0pt 15pt"><span style="font-size:10pt;font-family:Arial;color:rgb(51,51,51);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">To learn more about our research center, visit </span><a href="https://cai2r.net" style="text-decoration:none" target="_blank"><span style="font-size:10pt;font-family:Arial;color:rgb(17,85,204);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:underline;vertical-align:baseline;white-space:pre-wrap">https://cai2r.net</span></a></p><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">References</span></p><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">[1] </span><a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.26977" style="text-decoration:none" target="_blank"><span style="font-size:10pt;font-family:Arial;color:rgb(17,85,204);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:underline;vertical-align:baseline;white-space:pre-wrap">Learning a variational network for reconstruction of accelerated MRI data</span></a><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">. K. Hammernik et al. MRM, 2018.</span></p><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">[2] </span><a href="https://doi.org/10.1002/mrm.27355" style="text-decoration:none" target="_blank"><span style="font-size:10pt;font-family:Arial;color:rgb(17,85,204);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:underline;vertical-align:baseline;white-space:pre-wrap">Assessment of the generalization of learned image reconstruction and the potential for transfer learning</span></a><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">. F. Knoll et al. MRM, 2019.</span></p><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">[3] </span><a href="https://arxiv.org/pdf/1811.08839.pdf" style="text-decoration:none" target="_blank"><span style="font-size:10pt;font-family:Arial;color:rgb(17,85,204);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:underline;vertical-align:baseline;white-space:pre-wrap">fastMRI: An Open Dataset and Benchmarks for Accelerated MRI</span></a><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">. J. Zbontar et al. 2018.</span></p><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">[4] </span><a href="https://github.com/nyukat/breast_cancer_classifier" style="text-decoration:none" target="_blank"><span style="font-size:10pt;font-family:Arial;color:rgb(17,85,204);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:underline;vertical-align:baseline;white-space:pre-wrap">Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening</span></a><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">. N. Wu et al. IEEE TMI, 2019.</span></p><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">[5] </span><a href="https://arxiv.org/pdf/1906.02846.pdf" style="text-decoration:none" target="_blank"><span style="font-size:10pt;font-family:Arial;color:rgb(17,85,204);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:underline;vertical-align:baseline;white-space:pre-wrap">Globally-Aware Multiple Instance Classifier for Breast Cancer Screening</span></a><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">. Y. Shen et al. MLMI, 2019.</span></p><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">[6] </span><a href="https://github.com/nyukat/breast_density_classifier" style="text-decoration:none" target="_blank"><span style="font-size:10pt;font-family:Arial;color:rgb(17,85,204);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:underline;vertical-align:baseline;white-space:pre-wrap">Breast density classification with deep convolutional neural networks</span></a><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">. N. Wu et al. ICASSP, 2018.</span></p><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">[7] </span><a href="https://www.nature.com/articles/s41598-018-34817-6" style="text-decoration:none" target="_blank"><span style="font-size:10pt;font-family:Arial;color:rgb(17,85,204);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:underline;vertical-align:baseline;white-space:pre-wrap">Segmentation of the proximal femur from MR images using deep convolutional neural networks</span></a><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">. C. M. Deniz et al. Scientific Reports, 2018.</span></p><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">[8] </span><a href="https://arxiv.org/abs/1911.03740" style="text-decoration:none" target="_blank"><span style="font-size:10pt;font-family:Arial;color:rgb(17,85,204);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:underline;vertical-align:baseline;white-space:pre-wrap">On the design of convolutional neural networks for automatic detection of Alzheimer's disease</span></a><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">. S. Liu et al. 2019.</span></p><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">[9] </span><a href="http://arxiv.org/abs/1911.05567" style="text-decoration:none" target="_blank"><span style="font-size:10pt;font-family:Arial;color:rgb(17,85,204);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:underline;vertical-align:baseline;white-space:pre-wrap">DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation</span></a><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">. A. Kaku et al. 2019.</span></p><p dir="ltr" style="line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">[10] </span><a href="https://www.ncbi.nlm.nih.gov/pubmed/29782993" style="text-decoration:none" target="_blank"><span style="font-size:10pt;font-family:Arial;color:rgb(17,85,204);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:underline;vertical-align:baseline;white-space:pre-wrap">Generalized Recurrent Neural Network accommodating Dynamic Causal Modeling for functional MRI analysis</span></a><span style="font-size:10pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre-wrap">. Y. Wang et al. Neuroimage, 2018.</span></p>



</div>