来源：AI求职

University of Alberta

阿尔伯塔大学（University of Alberta），始建于 1908 年，是坐落于加拿大阿尔伯塔省会埃德蒙顿的一所世界顶尖研究型大学，是加拿大 U15 研究型大学联盟创始成员、世界大学联盟成员以及世界能源大学联盟成员。阿尔伯塔大学的人工智能专业在全球居于领先地位，全球顶级计算机科学机构排名 CSRankings 2010-2020 年度人工智能领域世界排名第 37 名，其中人工智能和机器学习世界第 6 名。强化学习之父 Rich Sutton、以及 Alpha Go 的主要作者大卫·席尔瓦 （David Silver）和黄士杰（Aja Huang）均来自阿尔伯塔大学。

The Project

Our project goal is to improve the clinical management of children and youth suffering from scoliosis by reducing cumulative X-ray exposure during their growing years, resulting in improved long-term health outcomes.

Scoliosis is an orthopaedic condition resulting in curvature and rotation of the spine. It typically first appears during adolescence, and affects females more than males. Asymmetry of the torso is one of the symptoms. Scoliosis is diagnosed and monitored using x-rays. Unfortunately, x-rays expose young patients to the effects of radiation including a documented increase in cancer risk. Surface topography (ST) is a non-invasive three dimensional (3D) assessment of the torso shape. Using a laser scanner, 3D images of the torso are acquired and the asymmetry of the torso is measured. The severity of scoliosis is then quantified using indices reflecting the symmetry of the torso.

Previous studies using surface topography with 2D measurements instead of the available 3D data were not able to accurately predict the severity of the spinal curvature. In our recent work, we introduced a novel 3D asymmetry technique that does not rely on markers placed on the torso or on simple 2D measurements. Our ST measures were able to quantify the severity and progression of scoliosis.

In the current proposal, we will develop artificial intelligence techniques to better use the surface topography parameters to estimate the actual shape of the underlying spinal curvature. The developed methods will be designed to ensure that no moderate/severe curves are missed and that all progressing curves are detected to make sure that patients are not missing important treatment opportunities, while dramatically reducing the x-ray radiation exposure to patients. This project involves an exciting multidisciplinary collaboration with research teams across engineering and medicine.

Qualifications of the applicant

1. Applicants must hold a Master’s of Science (or Engineering) degree in a relevant discipline (e.g., civil engineering, mechanical engineering, computer science, electrical/computer engineering, etc.). Candidates with a Bachelor’s of Science (or Engineering) degree may also be considered.

2. Applicants should be willing and able to register in the University of Alberta’s Ph.D. program in Mechanical Engineering (strong M.Sc. level candidates may also be considered).

3. Applicants should be highly motivated, well organized and passionate about research with good communication skills in English. Please refer to the U of A requirements for English Language Proficiency.

4. Applicants should have strong desire to conduct transformational research in close collaboration with clinical professionals. Applicants must have a minimum GPA of 3.3.

5. The position is open to Canadian Citizens, permanent residents of Canada, and International Students.

Start Date

January 2022 (earlier or later start dates may be considered)

How to Apply

Interested candidates may contact Dr. Lindsey Westover or Dr. Qipei Mei by email (lwestove@ualberta.ca or qipei@ualberta.ca) with a recent CV, transcripts, and cover letter.

推荐阅读

• 【重磅】斯坦福李飞飞《注意力与Transformer》总结，84页ppt开放下载！

• 北京大学智能计算与感知实验室招收博士、硕士、本科实习生

• 周志华教授：如何做研究与写论文？（附完整的PPT全文）

• 【硕博士推荐】国内有哪些不错的CV(计算机视觉)团队?

• 分层级联Transformer！苏黎世联邦提出TransCNN: 显著降低了计算/空间复杂度！

• 清华姚班教师劝退文：读博，你真的想好了吗？

• 常用 Normalization 方法的总结与思考：BN、LN、IN、GN

• 注意力可以使MLP完全替代CNN吗？ 未来有哪些研究方向？

重磅！DLer-计算机视觉&Transformer群已成立！

大家好，这是计算机视觉&Transformer论文分享群里，群里会第一时间发布最新的Transformer前沿论文解读及交流分享会，主要设计方向有：图像分类、Transformer、目标检测、目标跟踪、点云与语义分割、GAN、超分辨率、视频超分、人脸检测与识别、动作行为与时空运动、模型压缩和量化剪枝、迁移学习、人体姿态估计等内容。

进群请备注：研究方向+学校/公司+昵称（如Transformer+上交+小明）

???? 长按识别，邀请您进群！