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 Scientist to join our interdisciplinary group and work on novel machine learning methods for medical imaging. The Research Scientist will develop new research directions, building upon our ongoing research on machine learning methods for accelerated MRI [1, 2, 3], breast cancer detection [4, 5, 6] and musculoskeletal [7] and brain image [8, 9, 10] analysis. We are especially interested in candidates who want to work on model explainability, robustness and uncertainty.
Requirements include: - Passion for research on deep learning for medical imaging. - PhD in computer science, medical imaging, mathematics, physics, electrical engineering or a related discipline. An exception can be made for an extraordinary candidate without a PhD. - Advanced skills in Python and Tensorflow or PyTorch. - Advanced knowledge of machine learning and deep learning. - Advanced skills in using Linux. - Experience in working with medical imaging data is a big plus. Responsibilities will include: - Developing novel machine learning methods for medical imaging. - Collaborating with other research scientists, faculty members, postdocs and students at our center. Timeline, Salary, and Benefits Please apply no later than 3/29. We expect the appointed candidate to start not later than the autumn 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....@nyulangone.org <yvonne....@nyulangone.org>) and Krzysztof Geras (k.j.ge...@nyu.edu). Please use the string “[machine learning research scientist]” 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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