美国桑迪亚国家实验室计算机科学博士后职位
Sandia National Laboratories is the nation's premier science and engineering lab for national security and technology innovation with major facilities in Albuquerque, New Mexico and Livermore, California. We are a world-class team of scientists, engineers, technologists, post docs, and visiting researchers all focused on cutting-edge technology, ranging from homeland defense, global security, biotechnology, and environmental preservation to energy and combustion research, computer security, and nuclear defense. To learn more, please visit our website at www.sandia.gov.
Sandia National Laboratories is searching for a Postdoctoral Appointee in Optimization and UQ R&D for the Optimization and Uncertainty Quantification Department located in our Albuquerque, NM facility. This position is a temporary, full-time opportunity. Currently, this position does not require a DOE-granted security clearance.
Department Description
The mission of the Optimization and Uncertainty Quantification Department at Sandia National Laboratories is to provide leadership in the research, development, and application of scientific optimization and UQ algorithms and software. Staff members work in a collaborative, highly multidisciplinary, team-based environment. The department is well known for DAKOTA, a multilevel, parallel, object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analyses. Broad partnerships are maintained at many levels, and collaborations are actively carried out with other government institutions, universities and industry. The department frequently hosts students and faculty from world-class universities for extended visits through the Computer Science Research Institute (CSRI).
Job Summary
This position involves activities spanning fundamental algorithm research, object-oriented software development, and development and application of methodologies in the broad area of optimization and uncertainty quantification. The individual will work with a strong and growing multidisciplinary team to study the spectral and statistical properties of sample distributions, and algorithms for generating sample designs for optimization and uncertainty quantification. The position requires a highly motivated individual with a PhD in engineering, statistics or a related discipline, and strong academic and publication records. Also required is expertise in Bayesian statistics and Fourier analysis. Additionally, a versatile background in one or more of the following areas is desired: Mathematical modeling and optimization, sampling, meshing, signal and image processing, parameter estimation, wavelets, filter design, pattern recognition, and neural networks. The candidate should be proficient at programming and fluent in C++ and object oriented software design principles, and software prototyping and experimentation, for example in MATLAB. Research results are expected to be published in reports and leading technical journals, and presented at technical workshops and conferences. Results, when appropriate, are to be implemented into our optimization/UQ and mathematical software toolkits such as Trilinos and DAKOTA. These activities will be carried out in a collaborative, team-based environment.
Required
· Ph.D. in the field of Computational Science, such as mathematics, statistics, engineering or a related discipline, and have academic or work experience specializing in the targeted areas
· Research experience, as evidenced by technical publications and presentations, in the design and analysis of algorithms in the areas of optimization and uncertainty quantification
· Demonstrated expertise in Bayesian statistics and Fourier analysis
· Advanced C++ programming skills, with relevant software development experience in a team-based environment
Desired
· Proven ability to work in a collaborative, multi-disciplinary research and software development environment
· Understanding of formal software quality engineering principles
· Experience and/or training in one or more engineering application areas
· Knowledge and/or experience in sampling, meshing, signal and image processing, wavelets, filter design, pattern recognition, and neural networks
· Excellent written and oral communication skills
· Knowledge of advanced HPC architectures and operating systems
桑迪亚国家实验室是全国首屈一指的科学与工程实验室为国家安全和技术创新的主要设施在阿尔伯克基，新的墨西哥，利弗莫尔，加利福尼亚。我们是一个世界级的科学家，工程师，技术员，博士后团队，和客座研究人员都集中在尖端技术，从国土防卫，全球安全和环境保护，生物技术，能源和燃烧研究，计算机安全，国防和核。
桑迪亚国家实验室正在寻找优化和昆士兰大学研发的博士后人员的优化和不确定性的量化部设在阿尔伯克基，NM设施。这个职位是暂时的，全职的机会。目前，这个职位不需要获得安全许可DOE。
部门介绍
和不确定性的量化部门在桑迪亚国家实验室的优化的任务是在研究，开发和提供领导，科学优化和昆士兰大学的算法和软件应用。工作人员在一个协作，多学科，以团队为基础的环境。该部门是南达科他州，众所周知的一个多层次的，平行的，面向对象的框架，优化设计，参数估计，不确定性的量化，和敏感性分析。广泛的伙伴关系是保持多层次合作，并积极开展与其他政府机构，大学和工业。该部门经常举办学生和扩展访问世界一流大学教师在计算机科学研究所(CSRI)。
工作概要
这个职位需要生成的基本算法的研究活动，面向对象的软件开发，并在优化和不确定性的量化宽范围和应用发展的方法。个人将与一个强大和不断增长的多学科团队为研究样本的分布和统计特性的光谱，并产生用于优化和不确定性的量化样本设计算法。该职位要求高度的个人动机具有博士学位的工程，统计或相关学科，和较强的学术出版记录。还需要的是在贝叶斯统计和傅里叶分析的能力。此外，在以下一个或多个领域通用的背景要求：数学建模和优化，抽样，啮合，信号和图像处理，参数估计，小波，滤波器设计，模式识别，神经网络。考生应熟练编程和C++和面向对象的软件设计原则、软件FLUENT，原型和实验，例如在MATLAB。研究结果将发表在期刊的报告和领先的技术，并在技术研讨会和会议。结果，在适当的时候，要实现我们的优化/ UQ和数学软件工具包如Trilinos和南达科他州。这些活动将在合作进行，以团队为基础的环境。
要求
·博士在计算机科学领域，如数学，统计，工程或相关专业，有学术或专业领域经验的工作目标
·研究经验，通过技术出版物和演示证明，在优化和不确定性的量化的区域的算法分析与设计
·证明在贝叶斯统计和傅里叶分析的能力
