科研实习 | 麻省理工学院韩松教授实验室招收科研实习生（可远程）

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麻省理工学院

The success of deep learning comes from three aspects: efficient algorithm, powerful hardware, and large-scale dataset. Our lab targets the first two aspects. MIT HAN Lab is looking for motivated students in the area of deep learning and computer architecture to solve impactful AI problems with light-weight models and high computational efficiency. We have internship openings.Song Han is an assistant professor at MIT’s EECS. He received his PhD degree from Stanford University. His research focuses on efficient deep learning computing. He proposed “deep compression” technique that can reduce neural network size by an order of magnitude without losing accuracy, and the hardware implementation “efficient inference engine” that first exploited pruning and weight sparsity in deep learning accelerators. His team’s work on hardware-aware neural architecture search (ProxylessNAS, Once-for-All Network (OFA), MCUNet) was highlighted by MIT News, Wired, Qualcomm News, VentureBeat, IEEE Spectrum, adopted by PyTorch and AutoGluon, received six low-power computer vision contest awards in flagship AI conferences. Song received Best Paper awards at ICLR’16 and FPGA’17, Amazon Machine Learning Research Award, SONY Faculty Award, Facebook Faculty Award, NVIDIA Academic Partnership Award. Song was named “35 Innovators Under 35” by MIT Technology Review; he received the NSF CAREER Award and the IEEE “AIs 10 to Watch: The Future of AI” award.实验室主页：https://hanlab.mit.edu/导师个人主页：https://songhan.mit.edu/

岗位职责

机器学习模型优化和移动端部署。

实习收获

1. 韩松教授亲自指导机器学习领域相关研发技术；

2. 定期培训；

3. 表现优异者可获得韩松教授推荐信，优先录取到 MIT 读博。

岗位要求

1. 重点院校，在读研究生或大三大四本科生；

2. 每周实习时间 4 天以上，保证连续实习至少 3 个月（6 个月更佳）；

3. Familiar with Python and PyTorch

4. Experience of developing and training state-of-art deep learning models

5. Experience with developing large scale distributed machine learning systems and deployment on edge devices

6. Deep knowledge in computer vision and/or time-series analysis

加分项

1. Experience with machine learning model deployment with ONNX, TFLite, TensorRT, Qualcomm SNPE, etc

2. Experience with embedded Linux, Android, iOS development

3. Experience with AWS or other cloud services

 申请方式 

📪 [e-mail]

请将附有成绩排名的简历和一页纸的科研经历发送至邮箱。

简历及邮件格式命名：应聘 MIT 实习+姓名+研究生院校+本科院校+毕业时间+每周可实习天数，例如：应聘 MIT 实习-李刚-研究生北大-本科复旦-21 年毕业-每周五天

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实习内推

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