劳伦斯伯克利国家实验室招收机器学习研究博士后，要求物理学、计算机科学、数学等相关学科博士学历，对大型仪器如粒子加速器和同步加速器等感兴趣，对软件例程执行和仪器操作感兴趣;有Python, MATLAB, Octave等相关程序语言使用经验；有在跨学科研究尤其是科技领域合作共事的经历。其他申请要求还包括，熟悉机器学习、深度学习、神经网络等热门领域；仪器控制的算法反馈；掌握低级别的语言控制技能，如C++,C语言等。该博士后职位为期两年，博士后薪酬待遇将根据个人的研究经历及学习经历情况而定。
Lawrence Berkeley National Laboratory
Organization: AF-Accelerator Tech-Applied Phys
Berkeley Lab’s (https://www.lbl.gov/) Accelerator Technology and Applied Physics (ATAP, http://atap.lbl.gov/) Division has an opening for a Postdoctoral Fellow to join the team.
You will work in the ATAP Division (http://atap.lbl.gov/) together with the Advanced Light Source (ALS, https://als.lbl.gov/) at Lawrence Berkeley National Laboratory (Berkeley Lab) for the area of machine learning for accelerators. You will apply and develop machine learning (ML) schemes to enhance accelerator control as well as ML methods that support and enhance codes used for the design of future accelerators.
What You Will Do:
• Apply and develop ML schemes to enhance accelerator control and apply to the operating accelerator.
• Investigate ML methods that can support and/or enhance optimization codes used in the design of future accelerators.
• Work closely with ATAP and ALS scientists to develop new optimization techniques.
• Draft manuscripts for publication in peer-reviewed scientific journals.
• Participate actively in weekly meetings and seminars.
• Work independently as well as collaborate with other members of ATAP, ALS, and SLAC.
• Maintain an accurate and detailed scientific logbook of all software developed; ensure that others can duplicate results.
Additional Responsibilities as needed:
• Develop new ML algorithms.
• Embed ML algorithms into the accelerator control software for standard user operation.
• Embed ML algorithms into accelerator design codes/workflows (eg. MOGA).
What is Required:
• Ph.D. in Physics, Computer Science, Mathematics, or a related field.
• Interest in large-scale instrumentation such as particle accelerators and synchrotrons.
• Interest in implementation of software routines to operating instrumentation.
• Experience with Python, MATLAB, Octave, or similar.
• Ability to collaborate with a variety of technical and scientific staff in a diverse multidisciplinary team environment with excellent interpersonal skills.
• Strong written and verbal communication skills to present and disseminate scientific software developments at group meetings and conferences.
Additional Desired Qualifications:
• Familiarity with any of the following:
- ML, deep learning, neural networks, etc.
- Feedback or feed-forward algorithms for control of instrumentation.
- Implementation and/or application of high-level controls software for scientific instrumentation.
- Scientific data acquisition and/or archival as well as post-processing and analysis of acquired data.
- Low-level controls language skills (C++, C).
- Software version control systems (git, github, bitbucket).
The posting shall remain open until the position is filled.
• This is a full time, 2 year, postdoctoral appointment with the possibility of renewal based upon satisfactory job performance, continuing availability of funds and ongoing operational needs. Salary for Postdoctoral positions depends on years of experience post-degree.
• Full-time, M-F, exempt (monthly paid) from overtime pay.
• This position is represented by a union for collective bargaining purposes.
• Salary will be predetermined based on postdoctoral step rates.
• This position may be subject to a background check. Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment.
• Applicants should include a cover letter, a resume (CV) with a list of publications and presentations, as well as names of three references for future letters of recommendation with their application.
• Potential applicants who wish to discuss the position in more detail may contact the Principal Investigator, Dr. Simon C. Leemann, at SCLeemann@lbl.gov.