If you receive an employment opportunity from a colleague and you would like to share with the VISE community, please fill out this RedCap<\/a> form
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| Company<\/th> | Position<\/th> | Details<\/th> | Contact Information <\/th> | <\/th>\n<\/tr>\n<\/thead>\n |
|---|---|---|---|---|
| Intuitive Surgical<\/td> | Position in Multiport New Product Development<\/td> | Posted 3.27.26 \nFor this role we are looking for candidates with strong fundamentals in mechanics and control. Folks who are comfortable with the material presented in books from Siciliano, Spong, and Yoshikawa have the right kind of academic background. Experience putting the theory into practice with real hardware is also important. \nThe role involves coding and proficiency in C, Matlab, C++ (in that priority order) are also requirements. For software development, algorithm design and logical clarity are more important than being an expert in all the details of a particular language. Additionally, 'full stack' development consisting of stringing together existing libraries and applications is not particularly relevant. \n \nWebsite<\/a> \n<\/td> | Contact: Jason Pile Jason.Pile@intusurg.com<\/a><\/td> | <\/td>\n<\/tr>\n |
| Intuitive Surgical<\/td> | Machine Learning Intern<\/td> | Posted: 3.10.26 \n \nAre you passionate about designing software solutions that enable novel user interfaces and user interactions? Do you have a passion for working on real-time video on embedded systems? \n \nWe are looking for someone who thrives on collaboration and wants to push the boundaries of what is possible in surgical robotics today! Join our team of driven and dedicated software and computer vision engineers to develop real-time sensing and perception technologies that support novel human machine interfaces for next-generation robot-assisted surgery platforms. \n \nWe balance research and product to deliver state-of-the-art experiences, innovating through the full stack, and collaborating across hardware and software teams to influence computer vision based deep learning algorithms roadmap that brings our vision to life. \n \nThe successful candidate must be able to apply domain expertise in a focused, small-team environment, and must have a solid knowledge of state-of-the-art deep learning techniques used for vision applications. A strong sense of shared responsibility and shared reward is required. \n \nIntern will report to Senior Manager, Computer Vision & Machine Learning Algorithm Software, and main responsibility will be to implement state-of-the-art deep learning algorithms. \n \nWebsite<\/a> \n<\/td> | Contact: Dale Bergman Dale.Bergman@intusurg.com<\/a> \n<\/td> | <\/td>\n<\/tr>\n |
| Dannce.ai<\/td> | Paid Internship: Data Science - Computer Vision and Signal Processing for applications in Movement Disorder Care<\/td> | Role Overview We are seeking a Data Science Intern to join our team to lead a focused, end-to-end project aimed at producing best-in-class 3D human pose estimates from monocular video, evaluated against synchronized multi-camera ground-truth data. This internship centers on improving the accuracy, stability, and reliability of 3D pose estimates derived from single-camera video by leveraging a unique dataset that includes synchronized 6-camera ground-truth recordings and matched monocular video recordings. \nThe intern will build a reproducible, batchable pose-estimation pipeline, along with a quality-control (QC) system that can detect failures and dataset issues at scale. While recommended approaches and metrics are outlined, the intern will have meaningful freedom to explore alternatives and iterate based on experimental findings. \nQualifications \n\u25cf Current Master\u2019s or PhD student in Computer Science, Biomedical Engineering, Electrical Engineering, Data Science, Robotics, or a related field \n\u25cf Strong Python skills (NumPy, Pandas, scikit-learn, OpenCV, PyTorch preferred) \n\u25cf Experience with computer vision, pose estimation, or deep learning \n\u25cf Familiarity with time-series modeling, signal processing, or temporal ML models \n\u25cf Comfortable designing experiments and evaluating models quantitatively \n\u25cf Clear written and verbal communication skills \n\u25cf Nice to have: Experience with CV and pose estimation (e.g., MediaPipe, MMPose, Familiarity with temporal models for 3D pose (e.g., MotionBERT), Solid fundamentals in 3D geometry (camera models, intrinsics\/extrinsics, reprojection error; multi-view triangulation), Apple \/ iPhone depth: ARKit \/ RealityKit basics (sceneDepth \/ LiDAR), iOS data capture pipelines<\/td> | Interested candidates should submit the following to hiring@dannce.ai<\/a>: \n\u25cf A current resume or CV \n\u25cf A brief statement (1 paragraph) describing your interest in the role and why it feels like a good fit \n\u25cf References available upon request<\/td> | <\/td>\n<\/tr>\n |
| Dannce.ai<\/td> | Paid Internship: Data Science - Digital Signals in Neurology<\/td> | Role Overview We are seeking a Data Science Intern to join our team. The intern will adapt and extend existing analysis pipelines originally developed for Parkinson\u2019s Disease to apply them to Multiple Sclerosis and other disease areas. The focus will be on correlation analyses between digital features and clinician-scored outcomes, with the goal of contributing to a joint scientific publication. \nQualifications \n\u25cf Current Master\u2019s or PhD student in Biomedical Engineering, Computer Science, Data Science, Biostatistics, or related field \n\u25cf Strong skills in data analysis, machine learning, or statistics \n\u25cf Proficiency in Python (Pandas, NumPy, SciPy, scikit-learn) \n\u25cf Experience with time-series signal processing and clinical data analysis preferred \n\u25cf Strong communication and teamwork skills<\/td> | Interested candidates should submit the following to hiring@dannce.ai<\/a> \n \n\u25cf A current resume or CV \n\u25cf A brief statement (1 paragraph) describing your interest in the role and why it feels like a good fit \n\u25cf References available upon request \n<\/td> | <\/td>\n<\/tr>\n |
| VUMC Radiology<\/td> | Image Design Engineer<\/td> | We are seeking a technically strong Image Design Engineer to lead the end-to-end development of patient-specific anatomical models derived from DICOM CT\/MRI datasets. This role involves advanced image segmentation, 3D reconstruction, mesh optimization, and parametric CAD design to produce high-fidelity, dimensionally accurate models for surgical planning, device development, and clinical education. The engineer will collaborate closely with radiologists, surgeons, and multidisciplinary teams to translate clinical objectives into engineering specifications, optimize files for manufacturability, and manage multi-technology 3D printing workflows (SLA, FDM, MJP\/PolyJet), including printer calibration, materials selection, troubleshooting, post-processing, and quality assurance validation against source imaging. Strong spatial reasoning, attention to detail, and the ability to independently manage concurrent technical projects in a clinical innovation environment are essential. \nCONTACT POINT: Section Chief , Pediatric Radiology, Professor Radiology & Radiological Science Sumit Pruthi, MBBS \n<\/td> | sumit.pruthi@vumc.org<\/a> \n<\/td> | <\/td>\n<\/tr>\n |
| CELA<\/td> | Biomedical Engineer<\/td> | Job Description<\/a><\/td> | \n<\/td> | <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/p>\n","protected":false},"excerpt":{"rendered":" If you receive an employment opportunity from a colleague and you would like to share with the VISE community, please fill out this RedCap form<\/p>\n","protected":false},"author":670,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"spay_email":"","_links_to":"","_links_to_target":""},"tags":[],"acf":[],"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/P98pzF-31q","_links":{"self":[{"href":"https:\/\/www.vanderbilt.edu\/vise\/wp-json\/wp\/v2\/pages\/11620"}],"collection":[{"href":"https:\/\/www.vanderbilt.edu\/vise\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.vanderbilt.edu\/vise\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.vanderbilt.edu\/vise\/wp-json\/wp\/v2\/users\/670"}],"replies":[{"embeddable":true,"href":"https:\/\/www.vanderbilt.edu\/vise\/wp-json\/wp\/v2\/comments?post=11620"}],"version-history":[{"count":6,"href":"https:\/\/www.vanderbilt.edu\/vise\/wp-json\/wp\/v2\/pages\/11620\/revisions"}],"predecessor-version":[{"id":11778,"href":"https:\/\/www.vanderbilt.edu\/vise\/wp-json\/wp\/v2\/pages\/11620\/revisions\/11778"}],"wp:attachment":[{"href":"https:\/\/www.vanderbilt.edu\/vise\/wp-json\/wp\/v2\/media?parent=11620"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.vanderbilt.edu\/vise\/wp-json\/wp\/v2\/tags?post=11620"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}} |