英国伦敦大学学院博士后—图像分析/机器学习

英国伦敦大学学院博士后—图像分析/机器学习

伦敦大学学院（University College London，简称：UCL），位于英国伦敦，是一所公立研究型大学，也是第一所在伦敦成立的大学。学校是罗素大学集团和欧洲研究型大学联盟的成员，被誉为金三角名校之一。

伦敦大学学院于1826年2月11日正式成立，前身为伦敦大学，由于没有获得皇家宪章，它最初是作为股份公司成立的。1836年11月28日，学校获得皇家宪章，并更名为伦敦大学学院。同一天，一所新的伦敦大学成立，而且有权向该学校的学生授予医学、艺术和法律学位。1907年，学校根据1905年《伦敦大学学院（转让）法案》并入伦敦大学，更名为伦敦大学·大学学院，随后于1977年12月9日从伦敦大学中分离出来，并根据1979年《伦敦大学学院法案》重组为伦敦大学学院。2005年，伦敦大学学院获得了自行颁授独立学位的权利，不再使用伦敦大学的名义。

Research Fellow in Machine Learning for Multi-scale, Correlative, Biomedical Imaging

UCL

Location: London

Salary:    £43,124 to £51,610

Hours:    Full Time

Contract Type:      Fixed-Term/Contract

Placed On:     2nd January 2025

Closes:    24th January 2025

Job Ref:  B04-05786

About us

University College London (UCL) UCL is a multi-disciplinary university with a population of over 13,000 staff and 42,000 students from 150 different countries. Degree programmes are provided in Arts and Humanities, Built Environment, Brain Sciences, Engineering Sciences, Education, Laws, Life Sciences, Mathematical & Physical Sciences, Medical Sciences, Population Health Sciences and Social and Historical Sciences. For more information, please visit http://www.ucl.ac.uk/about UCL Mechanical Engineering UCL Mechanical Engineering has been pioneering the development of engineering education, having taught the core discipline for over 165 years. UCL was home to the UK’s first ever Professor of the Mechanical Principles of Engineering, Eaton Hodgkinson, in 1847. It was also where Sir Alexander Blackie William Kennedy introduced organized laboratory practicals in university education training; a world-leading educational innovation at the time.

About the role

Do you want to work in a team integrated cutting edge imaging and spatial transcriptomics of the human heart with state-of-the-art ML models to? We are looking for a Post-doctoral Research Fellow with an image analysis/Machine Learning (ML) background to work at integrating correlative high resolution 3D X-ray imaging and Spatial Transcriptomics. You will be based at University College London developing ML based piplines to i) segment 3D tissue strucutres from Hierarchical Phase-Contrast Tomography (HiP-CT) data (see mecheng.ucl.ac.uk/HiP-CT), ii) use these structures as a base line to perform registration between 3D HiP-CT, 2D histological sections and spatial transcriptomic. You will collaborate closely with the project team at Cambridge where spatial transciptomic data is being acquired and with collegues at the European Synchrotron (ESRF) in France where HiP-CT scans are performed.. This post is funded for 2 years in the first instance, with the possibility of extension. A job description and person specification can be accessed at the bottom of this page. If you wish to discuss the post informally, please contact Claire Walsh (c.walsh.11@ucl.ac.uk ) or for application process queries Ruikang Xue (ruikang.xue@ucl.ac.uk).

About you

The post will require a motivated researcher with experience in machine learning for segmentation and image registration. You will have a PhD and extensive knowledge and expertise in a relevant field. It is desirable to have experience in handling large imaging data. Your expertise should be at a level appropriate for the conduct of research and publishing new knowledge in leading international research journals. The post-holder will need to show a high level of initiative and an ability to work collaboratively and independently. Applicants should have good team-working skills and a strong command of English. You will join a dynamic international multidisciplinary group of academics, clinicians, beamline scientists, post-docs and PhD students developing and applying synchrotron X-ray and other techniques to study biological systems. You will report to Dr Claire Walsh & Prof. Peter Lee at UCL.

What we offer

For information about our rewards and benefits please visit https://www.ucl.ac.uk/work-at-ucl/reward-and-benefits.

Our commitment to Equality, Diversity and Inclusion

As London’s Global University, we know diversity fosters creativity and innovation, and we want our community to represent the diversity of the world’s talent. We are committed to equality of opportunity, to being fair and inclusive, and to being a place where we all belong. We therefore particularly encourage applications from candidates who are likely to be underrepresented in UCL’s workforce. These include people from Black, Asian, and ethnic minority backgrounds; disabled people and LGBTQI+ people.

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