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Position: Postdoctoral Appointee - Machine Learning
Institution: Argonne National Laboratory
Department: MCS-Mathematics and Computer Science
Location: Lemont, Illinois, United States
Duties: The Mathematics and Computer Science Division at Argonne National Laboratory seeks a postdoctoral appointee to perform machine/deep learning research and development for scientific discovery on some of the world’s fastest supercomputers. This work will include development of scalable CNNs for images, LSTMs for time series, Generative modeling, and Q-learning with flexibility on the class of methods pursued based on scientific applications in high energy physics, high performance computing, weather modeling, urban planning, and critical infrastructure analysis
Requirements: Ideal candidates are expected to have: Significant experience with machine learning and deep learning for image and/or spatio-temporal data. Significant experience in high-performance computing. Significant experience programming in one or more programming languages such as C, C++, and Python. Significant experience with Spark, scikit-learn, Keras, Tensorflow, Pytorch, or Chainer. Good communication skills both verbal and written
   
Text: Postdoctoral Appointee-Machine Learning Requisition Number: 403210 Location: Lemont, IL Functional Area: Research and Development Division: MCS-Mathematics and Computer Science Employment Category: Temporary 6 Months or Greater Education Required: Doctorate Degree Level (Grade): 700 Shift: 8:30 - 5:00 Share: Facebook LinkedIn Twitter Position Description The Mathematics and Computer Science Division at Argonne National Laboratory seeks a well-prepared postdoctoral appointee to perform machine/deep learning research and development for scientific discovery on some of the world’s fastest supercomputers. This work will include development of scalable CNNs for images, LSTMs for time series, Generative modeling, and Q-learning with flexibility on the class of methods pursued based on scientific applications in high energy physics, high performance computing, weather modeling, urban planning, and critical infrastructure analysis. You will actively collaborate with computer scientists and mathematicians and have the opportunity to build an independent research program. Position Requirements Position Requirements: Ideal candidates are expected to have:Significant experience with machine learning and deep learning for image and/or spatio-temporal data. Significant experience in high-performance computing. Significant experience programming in one or more programming languages such as C, C++, and Python. Significant experience with Spark, scikit-learn, Keras, Tensorflow, Pytorch, or Chainer. Good communication skills both verbal and written. Experience with measuring and characterizing the performance of parallel deep leanring applications on HPC plarforms. Software development practices and techniques for computational and data-intensive science problems. Bachelor’s and 5 years; Master’s and 3 years; Doctorate and 0 years, or equivalent Desirable knowledge and skills: Experience with BigData stack, including Apache Spark, and data analytics. Good experience and skills in interdisciplinary research involving computer scientists and discipline scientists. Experience with parallel programming such as MPI. Good ability to provide project leadership. Collaborative skills including the ability to work well with other laboratories and universities, supercomputer centers, and industry. As an equal employment opportunity and affirmative action employer, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne encourages minorities, women, veterans and individuals with disabilities to apply for employment. Argonne considers all qualified applicants for employment without regard to age, ancestry, citizenship status, color, disability, gender, gender identity, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.
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