外籍人才求职英文简历
第1篇：外籍人才求职英文简历
外籍人才求职英文简历
Stanford University, Stanford, CA
M.S. degree in Engineering Economic Systems and Operations Research in June 2000.
Ph.D. degree in Management Science and Engineering June 2004.
Dissertation title: “Multi-agent learning and coordination algorithms for distributed dynamic resource allocation.”
Dissertation advisor: Nicholas Bambos
Massachusetts Institute of Technology, Cambridge, MA
B.S. degree in Mathematics in June 1997.
M.S. degree in Systems Science and Control Engineering from the department of Electrical Engineering and Computer Science in June 1998. Master's thesis topic: Context-sensitive planning for autonomous vehicles operating in complex, uncertain, and nonstationary environments.
EXPERIENCE
Sun Microsystems Laboratories, Menlo Park, CA
April 2003 – Present:
http://research.sun.com/people/vengerov/resume_vengerov.doc
Conceiving, developing and implementing self-managing and self-optimizing capabilities in computer systems, covering domains such as: cache-aware thread scheduling and CPU power management, dynamic sharing of CPU/memory/bandwidth, dynamic data migration in distributed storage systems, dynamic job scheduling and job pricing in cloud computing, dynamic user migration in distributed virtual environments, etc.
Principal investigator for the Adaptive Optimization project since 2006.
Multiple patent applications filed, conference/journal papers published, multiple successful adaptive learning systems designed and implemented. The publicly available case studies are in the “technical reports” section of http://research.sun.com/people/vengerov/publications.html.
Intelligent Inference Systems Corp., Sunnyvale, CA Research Scientist
April 2002 – April 2003: Started a new research initiative in applying the ACFRL algorithm and the previously developed multi-agent coordination algorithms to power control in wireless networks. Published several conference papers on this topic. Results demonstrate an improvement by more than a factor of 2 in comparison with the algorithms used in IS-95 and CDMA2000 standards.
April 2002 – April 2003: Wrote a Phase I STTR proposal to the Office of Naval Research and received funding for the topic of “Perception-based co-evolutionary reinforcement learning for UAV sensor allocation.” Developed theoretical algorithms and designed a practical implementation strategy, which demonstrated excellent results in a high-fidelity robotic simulator. Published a conference paper.
October 1998 – April 2002: Wrote a proposal to the NASA Program in Thinking Systems and received multi-year funding for the topic of cooperation and coordination in multi-agent systems. Developed, evaluated, and published new Reinforcement Learning algorithms for dynamic resource allocation among distributed agents operating jointly in complex, uncertain, and nonstationary environments.
Fall 2000: Developed a new algorithm for single-agent learning in noisy dynamic environments with delayed rewards: Actor-Critic Fuzzy Reinforcement Learning (ACFRL). Published a conference and a journal paper with a convergence proof for ACFRL. US patent (number 6,917,925) was granted for the ACFRL algorithm on July 12, 2005.
ChainCast Inc., San Jose, CA
Aug 2000 – Oct 2000: Conducted a survey of techniques for dynamic updating of multicasting trees and suggested a novel approach based on using multi-agent learning.
NASA Ames Research Center, Moffet Field, CA Summer 1998: Designed a framework for multiple agents operating in a complex, uncertain, and nonstationary environment. Agents learn to improve their policies using fuzzy reinforcement learning.
SRI International, Artificial Intelligence Center, Menlo Park, CA
Summer 1998: Developed a methodology for representing a replanning problem in the space of plans as a reinforcement learning problem.
Bear, Stearns & Co., Inc. - Proprietory Trading Department, New York, NY
Summer 1996, 1997: Conducted a comprehensive study of time series forecasting models with neural networks. Recommended a hybrid model combining best features of the existing models and implemented it in C++.
Summer 1995: Developed a stock forecasting system based on conventional econometric techniques and implemented it in SAS language. Gained exposure to various proprietary trading models.
Alphatech, Inc., Burlington, MA
Feb 1997 - May 1997: Developed an algorithm for optimal control of macroeconomic systems described by simultaneous-time equations and implemented it in MATLAB.
Arthur Andersen, Inc., Boston, MA
Feb 1996 - May 1996: Developed an internal System Dynamics cashflow model of startup businesses. Gained experience in management level client interactions and in project presentation skills.
Summer 1996: Independently designed a game theoretic bid forecasting system in procurement auctions for a large construction company. The project involved extensive on-site client interactions during model development as well as a final presentation to the top level management.
Property & Portfolio Research, Inc., Boston, MA
Feb 1994 - May 1995: Designed a mortgage portfolio analysis model and implemented it in Visual Basic for Excel. Developed a methodology for grouping real estate time series using cluster and factor analyses in SPSS. Designed an optimal investment strategy for a class of mortgage backed securities based on the efficient frontier characteristics. Gained broad exposure to real estate markets and models.
Donaldson, Lufkin & Jenrette, Inc. -- Pershing Division, Jersey City, NJ
Summer 1994: Developed a stock forecasting system based on technical analysis and economic indicators. Developed a DJIA trading strategy based on S&P 500 futures and demonstrated its profitability.
MIT Laboratory for Information and Decision Systems, Cambridge, MA
Aug 1993 - May 1994: Developed a trading strategy for US Treasury bonds based on multi-resolution wavelet analysis. Demonstrated its profitability as compared to the conventional moving average models.
PROGRAMMING
C++, Java, MATLAB; Various packages for statistics, neural networks and system dynamics.
PUBLICATIONS
Published 13 papers in refereed conferences, 8 journal papers, 1 book chapter. The complete list, including technical reports, is available at http://research.sun.com/people/vengerov/publications.html.
PATENTS
Four patents granted, 10 patent applications are currently under review at the US Patent Bureau.
PERSONAL
United States Citizen. Fluent in Russian and English. Black belt and instructor in Tae Kwon Do.
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Last updated 5/26/2009
David Vengerov
第2篇：外籍人才求职英文简历模板
外籍人才求职英文简历模板
Stanford University, Stanford, CA
M.S. degree in Engineering Economic Systems and Operations Research in June 2000.
Ph.D. degree in Management Science and Engineering June 2004.
Dissertation title: “Multi-agent learning and coordination algorithms for distributed dynamic resource allocation.”
Dissertation advisor: Nicholas Bambos
Massachusetts Institute of Technology, Cambridge, MA
B.S. degree in Mathematics in June 1997.
M.S. degree in Systems Science and Control Engineering from the department of Electrical Engineering and Computer Science in June 1998. Master's thesis topic: Context-sensitive planning for autonomous vehicles operating in complex, uncertain, and nonstationary environments.
EXPERIENCE
Sun Microsystems Laboratories, Menlo Park, CA
April 2003 – Present:
http://research.sun.com/people/vengerov/resume_vengerov.doc
Conceiving, developing and implementing self-managing and self-optimizing capabilities in computer systems, covering domains such as: cache-aware thread scheduling and CPU power management, dynamic sharing of CPU/memory/bandwidth, dynamic data migration in distributed storage systems, dynamic job scheduling and job pricing in cloud computing, dynamic user migration in distributed virtual environments, etc.
Principal investigator for the Adaptive Optimization project since 2006.
Multiple patent applications filed, conference/journal papers published, multiple successful adaptive learning systems designed and implemented. The publicly available case studies are in the “technical reports” section of http://research.sun.com/people/vengerov/publications.html.
Intelligent Inference Systems Corp., Sunnyvale, CA Research Scientist
April 2002 – April 2003: Started a new research initiative in applying the ACFRL algorithm and the previously developed multi-agent coordination algorithms to power control in wireless networks. Published several conference papers on this topic. Results demonstrate an improvement by more than a factor of 2 in comparison with the algorithms used in IS-95 and CDMA2000 standards.
April 2002 – April 2003: Wrote a Phase I STTR proposal to the Office of Naval Research and received funding for the topic of “Perception-based co-evolutionary reinforcement learning for UAV sensor allocation.” Developed theoretical algorithms and designed a practical implementation strategy, which demonstrated excellent results in a high-fidelity robotic simulator. Published a conference paper.
October 1998 – April 2002: Wrote a proposal to the NASA Program in Thinking Systems and received multi-year funding for the topic of cooperation and coordination in multi-agent systems. Developed, evaluated, and published new Reinforcement Learning algorithms for dynamic resource allocation among distributed agents operating jointly in complex, uncertain, and nonstationary environments.
Fall 2000: Developed a new algorithm for single-agent learning in noisy dynamic environments with delayed rewards: Actor-Critic Fuzzy Reinforcement Learning (ACFRL). Published a conference and a journal paper with a convergence proof for ACFRL. US patent (number 6,917,925) was granted for the ACFRL algorithm on July 12, 2005.
ChainCast Inc., San Jose, CA
Aug 2000 – Oct 2000: Conducted a survey of techniques for dynamic updating of multicasting trees and suggested a novel approach based on using multi-agent learning.
NASA Ames Research Center, Moffet Field, CA Summer 1998: Designed a framework for multiple agents operating in a complex, uncertain, and nonstationary environment. Agents learn to improve their policies using fuzzy reinforcement learning.
SRI International, Artificial Intelligence Center, Menlo Park, CA
Summer 1998: Developed a methodology for representing a replanning problem in the space of plans as a reinforcement learning problem.
Bear, Stearns & Co., Inc. - Proprietory Trading Department, New York, NY
Summer 1996, 1997: Conducted a comprehensive study of time series forecasting models with neural networks. Recommended a hybrid model combining best features of the existing models and implemented it in C++.
Summer 1995: Developed a stock forecasting system based on conventional econometric techniques and implemented it in SAS language. Gained exposure to various proprietary trading models.
Alphatech, Inc., Burlington, MA
Feb 1997 - May 1997: Developed an algorithm for optimal control of macroeconomic systems described by simultaneous-time equations and implemented it in MATLAB.
Arthur Andersen, Inc., Boston, MA
Feb 1996 - May 1996: Developed an internal System Dynamics cashflow model of startup businesses. Gained experience in management level client interactions and in project presentation skills.
Summer 1996: Independently designed a game theoretic bid forecasting system in procurement auctions for a large construction company. The project involved extensive on-site client interactions during model development as well as a final presentation to the top level management.
Property & Portfolio Research, Inc., Boston, MA
Feb 1994 - May 1995: Designed a mortgage portfolio analysis model and implemented it in Visual Basic for Excel. Developed a methodology for grouping real estate time series using cluster and factor analyses in SPSS. Designed an optimal investment strategy for a class of mortgage backed securities based on the efficient frontier characteristics. Gained broad exposure to real estate markets and models.
Donaldson, Lufkin & Jenrette, Inc. -- Pershing Division, Jersey City, NJ
Summer 1994: Developed a stock forecasting system based on technical analysis and economic indicators. Developed a DJIA trading strategy based on S&P 500 futures and demonstrated its profitability.
MIT Laboratory for Information and Decision Systems, Cambridge, MA
Aug 1993 - May 1994: Developed a trading strategy for US Treasury bonds based on multi-resolution wavelet analysis. Demonstrated its profitability as compared to the conventional moving average models.
PROGRAMMING
C++, Java, MATLAB; Various packages for statistics, neural networks and system dynamics.
PUBLICATIONS
Published 13 papers in refereed conferences, 8 journal papers, 1 book chapter. The complete list, including technical reports, is available at http://research.sun.com/people/vengerov/publications.html.
PATENTS
Four patents granted, 10 patent applications are currently under review at the US Patent Bureau.
PERSONAL
United States Citizen. Fluent in Russian and English. Black belt and instructor in Tae Kwon Do.
第3篇：外籍人才的英文简历模板
stanford university, stanford, ca
m.s. degree in engineering economic systems and operations research in june 2000.
ph.d. degree in management science and engineering june 2004.
dissertation title: “multi-agent learning and coordination algorithms for distributed dynamic resource allocation.”
dissertation advisor: nicholas bambos
massachusetts institute of technology, cambridge, ma
b.s. degree in mathematics in june 1997.
m.s. degree in systems science a
第4篇：外籍人才服务合同
外教服务合同
甲方： 地址： 负责人： 电话：
乙方：
住所地： 负责人： 电话：
甲乙双方本着平等互利的原则，结成人才合作关系。甲方拥有国外人才资源，乙方作为用人单位提供就业岗位，经双方友好协商之后，就外教人才来华就业达成以下共识：
一、合作方式
根据乙方的具体人才需求，甲方，筛选出合适的外教人才，并对人才做出评价和推介之后，推荐给乙方。乙方通过审核、面试之后决定是否录用。
二、工作方式
甲方提供的人才可担任的工作方式：全职。
乙方根据自己的需求，可以提出不同的人才招聘需求。
三、费用说明和支付方式
乙方需要支付给甲方的中介费用如下：
乙方付给甲方6500元人民币/人，作为甲方给乙方推荐人才的服务费用，甲方开具相关发票给乙方。
支付方式：
乙方接到外籍老师，确认该外籍老师就是被推荐的外籍老师本人。在外籍老师到乙方全职工作一个月后，由乙方一次性支付给甲方全部的中介服务费。
四.合作流程:
签定合作协议→选简历→面试→签订劳务合同→入职→支付中介费
五、双方责任
（一）甲方：
1．确保引进的海外人才质量。
2.根据乙方的需求，推荐
