机器学习医学影像方向，伦敦帝国理工秦宸博士组招收博士生（含奖学金）

新的一期博士招生正式启动！本期我们将为大家介绍伦敦帝国理工学院电气电子工程系助理教授秦宸博士招收博士生的相关信息。

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本期的招募信息来自 伦敦帝国理工学院电气电子工程系助理教授秦宸博士，欢迎对机器学习、医学影像计算及分析方向感兴趣的同学踊跃申请。

Imperial College London

Department of Electrical and Electronics Engineering & Imperial-X

Applications are invited for a PhD studentship in the field of machine learning in medical imaging, which will be jointly hosted by Department of Electrical and Electronics Engineering and the College’s new I-X initiative. The PhD position is partially funded for international students and fully funded for home student (UK). It is especially targeted at PhD applicants with an interest in artificial intelligence and healthcare. The successful candidate will also join the Biomedical Image Analysis Group (https://biomedia.doc.ic.ac.uk/).

The PhD research will explore the important topics of machine learning solutions for improving medical imaging workflow. Particularly, the research project will focus on investigating advanced deep learning approaches for inverse problems. It will develop approaches that can incorporate explicit prior knowledge into deep learning methods for data-efficient and interpretable learning. The aim of the research is to combine the best of knowledge-driven and data-driven approaches for inverse medical imaging problems, such as image reconstruction and image registration. The research is at the intersection of artificial intelligence and healthcare and has the potential to make significant positive impact on society by improving patient care through better diagnosis and treatment.

Department of Electrical and Electronics Engineering has a long and proud history of world-class research and innovation and is at the forefront of tackling the most urgent global challenges in energy, healthcare, smart technology, and communications. It ranked the 1st in the UK (Engineering) in REF 2021 based on the proportion of world-leading research (4*). 

I-X (Imperial-X) is a new collaborative environment for research, education, and entrepreneurship across the areas of artificial intelligence, machine learning, data science, statistics, and digital technologies. The goal of I-X is to realise new models for research, education, and entrepreneurship that go beyond traditional siloes imposed by academic disciplines, thus forming a blueprint for the university of the future. I-X benefits from a strategic investment by the College, which includes new facilities on Imperial’s White City and South Kensington campuses. For more information about I-X, please visit  https://ix.imperial.ac.uk.

Qualification

Applicants are expected to have a First Class or Distinction Masters level degree, or equivalent, in a relevant scientific or technical discipline, such as computer science, mathematics or engineering. Applicants should also meet the minimum requirement as outlined in the guidance on qualifications http://www.imperial.ac.uk/study/pg/apply/requirements/pgacademic/. Applicants must be fluent in spoken and written English. Good team-working, observational and communication skills are essential. Experience in one or more of the following areas is desired: machine learning, deep learning, mathematical modelling, and software engineering.

How to apply

Apply here: https://www.imperial.ac.uk/electrical-engineering/study/phd/. 

For further details of the post, please contact Dr Chen Qin (https://sites.google.com/view/chen-qin), at c.qin15@imperial.ac.uk. Early applications are encouraged. The post is preferred for candidates who can start in early 2023.  

Closing date: until post filled.

We are committed to equality and valuing diversity. We are also an Athena SWAN Silver Award winner, a Stonewall Diversity Champion, a Two Ticks Employer, and are working in partnership with GIRES to promote respect for trans people. We encourage applicants from underrepresented backgrounds to apply.

个人简介

秦宸，博士，现任伦敦帝国理工学院电气电子工程系助理教授, 此前任职英国爱丁堡大学助理教授。其于 2020 年 1 月于伦敦帝国理工大学计算机系取得博士学位，并从事博士后研究。在此之前，于清华大学取得硕士学位，并于哈尔滨工业大学取得本科学位。其研究方向为机器学习与医学影像的交叉学科研究，具体侧重机器学习在核磁共振图像加速成像及重构，以及医学图像分析中的研究。其研究受到了学术界及工业界的广泛关注，并且多次受邀在国际会议及研讨会中发表主题演讲。秦宸博士担任 MICCAI 2022 Area Chair 并曾获 Facebook AI Research/NYU fastMRI 挑战赛亚军等。近五年在 IEEE TMI, Medical Image Analysis, MICCAI, IPMI, ISMRM, ICCV 等工学及医学影像相关会议和期刊发表论文四十余篇，谷歌学术引用一千七百余次，并且担任多个国际会议、期刊和基金审稿人。个人主页：https://sites.google.com/view/chen-qin

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