Assistant Researcher - Machine Learning and AI - Electrical Engineering
Salary range: The posted UC academic salary scales (https://www.ucop.edu/academic-personnel-programs/compensation/2022-23-academic-salary-scales.html) set the minimum pay determined by rank and/or step at appointment. See the following table for the salary scale for this position https://www.ucop.edu/academic-personnel-programs/_files/2022-23/july-2022-salary-scales/t14-b.pdf. A reasonable estimate for this position is $110,900-$139,300 annually.
Percent time: 100%
Position duration: One year with the possibility of extension based on programmatic need, funding availability and satisfactory performance.
Open date: March 16, 2023
Next review date: Friday, Mar 31, 2023 at 11:59pm (Pacific Time)
Apply by this date to ensure full consideration by the committee.
Final date: Monday, Apr 17, 2023 at 11:59pm (Pacific Time)
Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled.
Prof. Keutzer's research group focuses on full-stack Deep Learning for a variety of applications. Full stack implies the ability to work from applications constraints, through Neural Net design and optimization, the development of library support for novel operators, efficient mapping onto hardware, and, occasionally, full hardware/software co-design of Neural Net accelerators. Applications of interest include Natural Language Processing, Recommendation Systems, 2D and 3D Computer Vision, Speech, and Computational Finance.
The candidate will need to take on several responsibilities as follows:
- Perform research in Machine Learning and AI focused on full stack machine learning for Speech Recognition and Natural Language Processing. This entails research in Neural Network architecture design, computationally efficient methods for training, algorithmically robust training algorithms, efficient inference, hardware co-design, and deployment on hardware. As the project progresses it is expected that the appointee will secure independent research funding and will develop an independent research program.
- It is expected for the researcher to be an expert in all of these areas to be able to contribute to full stack research that combines these methods
- Actively work on developing systematic approaches to tackle the full stack deep learning which requires strong mathematical background in optimization and loss landscape analysis including familiarity with high order (second order) methods
- Work closely with undergraduate, Masters, and PhD students both in terms of helping them get up to speed with the latest state-of-the-art and coding, as well as collaborating on the ML Systems research topics stated above
- Actively participate in writing grants and proposals and engage with research sponsors
- Publish results of research in peer-reviewed publications
Labor Contract: https://ucnet.universityofcalifornia.edu/labor/bargaining-units/ra/index.html
Basic qualifications (required at time of application)
PhD or equivalent international degree
Additional qualifications (required at time of start)
Minimum of three years postdoctoral experience and a publication record.
- PhD degree in computer science or related fields
- Five years of postdoctoral experience is preferred
- Proven publication track in Machine Learning Systems (MLSys, SuperComputing, or similar top conferences) and AI field (ICML, NeurIPS, ICLR, or similar top conferences)
- Publication record in areas of Natural Language Processing and Speech Recognition with a focus on efficient deployment and training
- Track record of mentoring Masters/PhD students in research related to AI Systems
- Publications in optimization with second order methods for machine learning applications
- Publication in analyzing loss landscape with second order methods
- Strong background in Mathematics and optimization including Higher Order Methods
- Curriculum Vitae - Your most recently updated C.V.
- Cover Letter (Optional)
- Statement of Research
- 3 required (contact information only)
Apply link: https://aprecruit.berkeley.edu/JPF03823
Help contact: firstname.lastname@example.org
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To apply, visit https://aprecruit.berkeley.edu/JPF03823