美国哈佛医学院麻省总医院博士后职位---机器学习

美国哈佛医学院麻省总医院博士后职位---机器学习

麻省总医院（Massachusetts General Hospital）建立于1811年，是坐落于波士顿的一家综合型医院。麻省总医院是美国排名第一的研究型医院，是美国历史悠久的医院之一，也是哈佛大学医学院下属的教学医院。

Postdoctoral Fellowship in Machine Learning

Massachusetts General Hospital / Harvard Medical School

Location: Boston, MA

Job Number: 7137030

Posting Date: Mar 29, 2023

Application Deadline: Open Until Filled

Job Description

Postdoctoral Fellow for Machine Learning @ Massachusetts General Hospital / Harvard Medical School

We seek a highly motivated Postdoctoral Fellow with strong machine learning (ML) background to work on analysis of electronic health records for cardiovascular disease.

The applicant will work with an inter-disciplinary team of researchers and clinicians within the Mass General Brigham system and Harvard community. Massachusetts General Hospital is the largest teaching hospital of Harvard Medical School.

An ideal candidate will have a background in Computer Science, Machine Learning, Statistics, or a related field. A Ph.D. degree or equivalent is required. In addition, she or he has high competency in python, and will have demonstrated an outstanding capability for independent research. Strong programming skills and a solid publication record in machine learning, AI/ ML conferences or healthcare conferences and journals is desirable.

Duties will include conducting original research, publishing in top-tier clinical and machine learning journals/conferences, mentoring students, and collaborating on research grant proposal writing.

How to Apply

To apply, please email us your application:

The application should include:

1. A research statement (max 1 page) including following 3 sections:

• the specific areas of ML you have worked on,

• how you will apply the above ML methods to electronic health records in this project

• your background and motivation for applying for this position

2. your preferred start date

3. Your current CV with research background and a list of your publications;

4. pdfs for two 2 representative publications.

5. Email addresses for two references (e.g. previous research supervisors and collaborators).

Contact Email: waghsk@gmail.com

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