• 论坛首页
  • 我的丁香客
  • 找人
    查找好友
  • 更多
    丁香园
    丁香通
    丁香人才
    丁香会议
    丁香搜索
    丁香医生
    丁香无线
    丁香导航
    丁当铺
    文献求助
    医药数据库
    丁香诊所
    来问医生
登录 注册

飘洋过海

关注今日:13 | 主题:198634
论坛首页  >  飘洋过海   >  公派留学
  • 发帖
    每发1个新帖
    可以获得0.5个丁当奖励
  • 回帖

分享到:

  • 微信

    微信扫一扫

  • 微博
  • 丁香客
  • 复制网址

【博后,访学,联合培养】哈佛医学院和波士顿儿童医院;医学图像+人工智能

  • 只看楼主
  • 页码直达:
  • 直达末页
楼主 dxy_596077s9
dxy_596077s9
入门站友

  • 0
    积分
  • 0
    得票
  • 0
    丁当
  • 1楼
这个帖子发布于3年零283天前,其中的信息可能已发生改变或有所发展。


关键词:医学图像分析处理,人工智能图像诊断,大脑图像神经科学,机器学习,临床预测


Postdoctoral and Visiting Scholars in Imaging Informatics

Postdoctoral research fellows and visiting student/scholar positions are available in the Fetal-Neonatal Neuroimaging Data Science Center (FNNDSC) at Boston Children’s Hospital (BCH) and Harvard Medical School (HMS), in the Longwood Medical District of Boston, Massachusetts, USA.

Team: 

The candidates will be suited in a dynamic and closely collaborating multidisciplinary team consisting of radiologists, pediatricians, psychiatrists and computer scientists, and build on the group’s previous work on neuroimaging analysis. Specifically working with this fellow will be
       - Yangming Ou, PhD, Instructor of Radiology and Pediatrics at Harvard Medical School, who is a computer and biomedical engineer focusing on developing image analysis and machine learning algorithms and tools, a member of FNNDSC at BCH and Psychiatric at MGH. Dr. Ou will mentor in algorithm development and daily research;
       - Randy Gollub, MD, PhD, Associate Professor of Psychiatry and Radiology at Harvard Medical School, who is a neuroimaging and medical informatics scientist and Associate Director of Psychiatric Neuroimaging at Massachusetts General Hospital. Dr. Gollub will mentor in research and collaborations.
       - Ellen Grant, MD, Professor of Pediatrics and Radiology at Harvard Medical School, who is a clinical radiologist, a pediatric neuroimaging researcher, and the Founding Director of FNNDSC. Dr. Grant will oversee scholars’ research, clinical directions and career development.


This team collaborates with MGH Martinos Biomedical Imaging Center, the newly established MGH/BWH Clinical Data Science Center, the newly established Harvard Medical School Biomedical Informatics Department, MGH Radiation Oncology, and MGH/BCH Neonatology and Neurology. So, the algorithms/tools in the following topics will have high impacts in research and applications.

Topics: 

  1. (Data Science in Radiology) Learning-based Abnormality Detection in Pediatric MRI. Pediatric brain MRI is the clinical gold standard for diagnosing perinatal brain injury. Expert interpretation has 20-50% uncertainty. We aim to develop a generic and computerized abnormality detection algorithm/tool for radiology assistance. This work will build on our recent image analysis and atlasing work, and will utilize advancements in machine learning. 

  2. (Data Science in Radiology) Learning-based Imaging Biomarkers for Predicting Clinical Variables. We aim to develop a generic framework and tool for imaging-based association with and prediction of clinical variables. Example clinical variables in our collaborations include: survival; longitudinal neuro-cognitive outcome (IQ and subcategory scores); genetic subtypes; treatment effects, disease prognosis, and more. Applications are in both pediatric and adult disease domains.

  3. (Data Science in Neuroscience) Quantifying Early-life Normative Brain Development. To use in-house and public data mining and image analysis tools to construct age-specific normal brain atlases, as well as to quantify normative structure and/or diffusion connectivity and covariant development in early life. 

Candidates: 

We are looking for excellent scholars having PhD degrees or in the PhD thesis stages, from Computer Science, Electrical Engineering, Biomedical Engineering, or a similar engineering disciplines. We seek motivated candidates in research, publication, creative thinking and real-world problem solving. Having an inter-disciplinary background in image processing, computer vision, machine learning, and programming (LINUX, C++, python/bash/perl scripting) is a strong plus. Experience in the three topics mentioned above is a plus but not required. Candidates with self funding are encouraged to apply. Harvard Medical School and its affiliated hospitals are equal opportunity employers.

To Apply: 

Please email your CV to Drs. Yangming Ou (yangming.ou@childrens.harvard.edu) and Ellen Grant (ellen.grant@childrens.harvard.edu).

 




版主xucong0567留言:
帮你改了分类,放在资源里大家看不到



  • jobs_postdoc_visitingscholar_2017.pdf(247.45k)
  • 邀请讨论
  • 不知道邀请谁?试试他们

    换一换
2017-05-23 03:51 浏览 : 1486 回复 : 0
  • 投票
  • 收藏 5
  • 打赏
  • 引用
  • 分享
    • 微信扫一扫

    • 新浪微博
    • 丁香客
    • 复制网址
  • 举报
    • 广告宣传推广
    • 政治敏感、违法虚假信息
    • 恶意灌水、重复发帖
    • 违规侵权、站友争执
    • 附件异常、链接失效
    • 其他
dxy_596077s9 编辑于 2017-05-24 14:28
  • • 用更宏大的视角看待医学——解读大咖宏见

关闭提示

需要2个丁当

丁香园旗下网站

  • 丁香园
  • 用药助手
  • 丁香通
  • 文献求助
  • 丁香人才
  • 丁香医生
  • 丁香导航
  • 丁香会议
  • 手机丁香园
  • 医药数据库

关于丁香园

  • 关于我们
  • 丁香园标志
  • 友情链接
  • 联系我们
  • 加盟丁香园
  • 版权声明
  • 资格证书

官方链接

  • 丁香志
  • 丁香园新浪微博
引用回复