美国犹他州立大学(USU)招收计算机工程博士 waive GRE
19290
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犹他州立大学(Utah State University) ECE系招收两名全奖博士生，主做probablistic model checking算法，应用于synthetic biology/nano方向。具体信息请参考下面的英文介绍。
这一段是我本人的评价：我朋友性格非常好，做学术也很认真扎实。关于Probablistic model checking大概内容可以查询Chris J Myers的paper。个人认为偏码，工业界来讲最对口的是EDA以及markov chain理论为基础的各大商业软件(譬如声音识别)。以后想转纯码或者DS也不难。USU的确专排和综排都不算高，所以明显over qualified的同学们可以考虑把机会留给别的同学哈
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Two Open PhD positions in Probabilistic Verification Methodologies for Synthetic Biology and Nanotechnology at Utah State University
Both Ph.D. positions are available at Utah State University in the U.S. They are fully funded Ph.D. positions for three years with the possibility of one-year extension. The expected starting date is late August, 2021. GRE requirement is temporarily suspended until further notice. Graduate application deadline closes on May 1st, 2021.
Abstract:
Synthetic biology and nanotechnology place increasing demands on design methodologies to ensure dependable and robust operation. Consisting of noisy and unreliable components, these complex systems have large and often infinite state spaces that include extremely rare error states. Probabilistic model checking techniques have demonstrated significant potential in quantitatively analyzing such system models under extremely low probability. Unfortunately, they generally require enumerating the model's state space, which is computationally intractable or impossible. Therefore, addressing these design challenges in emerging technologies requires enhancing the applicability of probabilistic model checking. Motivated by this problem, this project investigates an automated probabilistic verification framework that integrates approximate probabilistic model checking and counterexample-guided rare-event simulation to improve the analysis accuracy and efficiency. This multi-institution collaborative project focuses on verifying infinite-state continuous-time Markov chain (CTMC) models with rare-event properties. It addresses the scalability problem by first applying property-guided and on-the-fly state truncation techniques to prune unlikely states to obtain finite state representations that are amenable to probabilistic model checking. In the case of false or indeterminate verification results, probabilistic counterexamples are generated and utilized to improve the accuracy of the state reductions. Furthermore, it mines these critical counterexamples as automated guidance to improve the quality and efficiency for rare-event probabilistic simulations. This verification framework will be integrated within existing state-of-the-art probabilistic model checking tools (e.g., the PRISM model checking tool), and benchmarked on a wide range of real-world case studies in synthetic biology and nanotechnology.
========================================
Project description:
The first position will be advancing and developing efficient state space truncation and model abstraction techniques for the infinite-state CTMC models. In particular, we are interested in investigating:
- Algorithms for state space truncation for infinite-state probabilistic systems with improved accuracy;
- Prototype implementation of the developed algorithms in Java;
- Evaluation of the prototype on case studies in synthetic biology; and
- Predicate abstraction and SAT/SMT solving techniques for CTMC models.
The second position will investigate methodologies for rare-event stochastic simulations. In particular, we are interested in investigating:
- Counterexample-guided importance sampling techniques;
- Prototype software implementation; and
- Benchmark stochastic computing circuits in nanotechnology.
========================================
Qualifications:
Applicants must have a bachelor's degree in Electrical/Computer Engineering, Computer Science, or a related field. The successful candidate is expected to demonstrate strong background and interest in formal methods and algorithms, and preferably basic knowledge of probability and random process. She/He should be confident in independently developing academic software tools. Good writing and presentation skills in English are important as well. Knowledge of synthetic biology is preferred, but not required.
========================================
Salary:
Successful PhD candidate receives $1,600 per month. The pay is negotiable. The candidate is expected to work on average 20 hours per week during fall and spring semesters, and up to 40 hours per week during the summer. As a graduate student, you will receive a full tuition waiver. Additionally, you will receive student insurance coverage. Depending on funding situation, tuition differential and fees may also be covered.
========================================
ECE Department at USU:
The place of employment is the Electrical and Computer Engineering Department at Utah State University. The main campus is located in Logan, Utah, 88 miles (about 142 km) north of Salt Lake City. The mission of the Department of Electrical and Computer Engineering is to serve society through excellence in learning, discovery, and outreach. We provide undergraduate and graduate students an education in electrical and computer engineering, and we aspire to instill in them attitudes, values, and visions that will prepare them for lifetimes of continued learning and leadership in their chosen careers. Through research, the department strives to generate and disseminate new knowledge and technology for the benefit of the State of Utah, the nation, and beyond. The detailed graduate program description can be found at: engineering.usu.edu application information is available at: engineering.usu.edu
========================================
Additional Information about Logan:Logan is a valley community of about 125,000 people nestled in between the Wellsville Mountains and Bear River Range in northeastern Utah. The many ski resorts, lakes, rivers, and mountains in the region make it one of the finest outdoor recreation environments in the nation. With views of a natural area reserve from campus, the pristine natural environment of the area makes Logan one of America’s most attractive and affordable university towns ( explorelogan.com).
========================================
Contact:
For questions about this position, please contact: Dr. Zhen Zhang (1point3acres.com) and Dr. Chris Winstead (1point3acres.com).
犹他州立大学(Utah State University) ECE系招收两名全奖博士生，主做probablistic model checking算法，应用于synthetic biology/nano方向。具体信息请参考下面的英文介绍。
这一段是我本人的评价：我朋友性格非常好，做学术也很认真扎实。关于Probablistic model checking大概内容可以查询Chris J Myers的paper。个人认为偏码，工业界来讲最对口的是EDA以及markov chain理论为基础的各大商业软件(譬如声音识别)。以后想转纯码或者DS也不难。USU的确专排和综排都不算高，所以明显over qualified的同学们可以考虑把机会留给别的同学哈
-------------------
Two Open PhD positions in Probabilistic Verification Methodologies for Synthetic Biology and Nanotechnology at Utah State University
Both Ph.D. positions are available at Utah State University in the U.S. They are fully funded Ph.D. positions for three years with the possibility of one-year extension. The expected starting date is late August, 2021. GRE requirement is temporarily suspended until further notice. Graduate application deadline closes on May 1st, 2021.
Abstract:
Synthetic biology and nanotechnology place increasing demands on design methodologies to ensure dependable and robust operation. Consisting of noisy and unreliable components, these complex systems have large and often infinite state spaces that include extremely rare error states. Probabilistic model checking techniques have demonstrated significant potential in quantitatively analyzing such system models under extremely low probability. Unfortunately, they generally require enumerating the model's state space, which is computationally intractable or impossible. Therefore, addressing these design challenges in emerging technologies requires enhancing the applicability of probabilistic model checking. Motivated by this problem, this project investigates an automated probabilistic verification framework that integrates approximate probabilistic model checking and counterexample-guided rare-event simulation to improve the analysis accuracy and efficiency. This multi-institution collaborative project focuses on verifying infinite-state continuous-time Markov chain (CTMC) models with rare-event properties. It addresses the scalability problem by first applying property-guided and on-the-fly state truncation techniques to prune unlikely states to obtain finite state representations that are amenable to probabilistic model checking. In the case of false or indeterminate verification results, probabilistic counterexamples are generated and utilized to improve the accuracy of the state reductions. Furthermore, it mines these critical counterexamples as automated guidance to improve the quality and efficiency for rare-event probabilistic simulations. This verification framework will be integrated within existing state-of-the-art probabilistic model checking tools (e.g., the PRISM model checking tool), and benchmarked on a wide range of real-world case studies in synthetic biology and nanotechnology.
========================================
Project description:
The first position will be advancing and developing efficient state space truncation and model abstraction techniques for the infinite-state CTMC models. In particular, we are interested in investigating:
- Algorithms for state space truncation for infinite-state probabilistic systems with improved accuracy;
- Prototype implementation of the developed algorithms in Java;
- Evaluation of the prototype on case studies in synthetic biology; and
- Predicate abstraction and SAT/SMT solving techniques for CTMC models.
The second position will investigate methodologies for rare-event stochastic simulations. In particular, we are interested in investigating:
- Counterexample-guided importance sampling techniques;
- Prototype software implementation; and
- Benchmark stochastic computing circuits in nanotechnology.
========================================
Qualifications:
Applicants must have a bachelor's degree in Electrical/Computer Engineering, Computer Science, or a related field. The successful candidate is expected to demonstrate strong background and interest in formal methods and algorithms, and preferably basic knowledge of probability and random process. She/He should be confident in independently developing academic software tools. Good writing and presentation skills in English are important as well. Knowledge of synthetic biology is preferred, but not required.
========================================
Salary:
Successful PhD candidate receives $1,600 per month. The pay is negotiable. The candidate is expected to work on average 20 hours per week during fall and spring semesters, and up to 40 hours per week during the summer. As a graduate student, you will receive a full tuition waiver. Additionally, you will receive student insurance coverage. Depending on funding situation, tuition differential and fees may also be covered.
========================================
ECE Department at USU:
The place of employment is the Electrical and Computer Engineering Department at Utah State University. The main campus is located in Logan, Utah, 88 miles (about 142 km) north of Salt Lake City. The mission of the Department of Electrical and Computer Engineering is to serve society through excellence in learning, discovery, and outreach. We provide undergraduate and graduate students an education in electrical and computer engineering, and we aspire to instill in them attitudes, values, and visions that will prepare them for lifetimes of continued learning and leadership in their chosen careers. Through research, the department strives to generate and disseminate new knowledge and technology for the benefit of the State of Utah, the nation, and beyond. The detailed graduate program description can be found at: engineering.usu.edu application information is available at: engineering.usu.edu
========================================
Additional Information about Logan:Logan is a valley community of about 125,000 people nestled in between the Wellsville Mountains and Bear River Range in northeastern Utah. The many ski resorts, lakes, rivers, and mountains in the region make it one of the finest outdoor recreation environments in the nation. With views of a natural area reserve from campus, the pristine natural environment of the area makes Logan one of America’s most attractive and affordable university towns ( explorelogan.com).
========================================
Contact:
For questions about this position, please contact: Dr. Zhen Zhang (1point3acres.com) and Dr. Chris Winstead (1point3acres.com).
