院系简介
MS in System Engineering开设在工程学院的系统工程系下，“智能”建筑和高速公路，环境监测，传感器网络，混合动力系统。这些只是众多电气和系统工程融合方式中的一小部分，特别是在宾夕法尼亚州。系统工程项目以电气和系统工程的交叉为基础，给学生提供技术系统日益复杂所需的深入的理论基础和跨学科技能。
项目灵活的课程使学生能够根据个人兴趣和目标调整自己的学习，从信号处理、优化、模拟、控制和控制论到复杂的自适应系统、随机过程和决策科学。毕业生在洛克希德马丁等大公司担任领DAO职务，或在宾夕法尼亚大学或其他主要研究大学攻读博士学位。
项目是为那些将在日益复杂的系统工程领域成为领DAO者的高素质学生而设计的。入学学生通常拥有工程学、数学、物理或经济科学的学士学位。拥有其他专业学位且在定量分析和计算机分析方面有专长的学生也被该项目录取。
项目详解
MS in System Engineering
课程设置
Ø 时长：1-2年。要求完成10门课。如果学生每学期学四门课，暑期学两门课，可以在一年内完成项目。学生也可以每学期选择三门课程（前两学期）和四门课程（一个学期），可以在1.5年内完，或者每学期完成三门课程，最后一学期完成一门课程，可以两年毕业（两年完成学位的情况对国际生不适合，因为国际生每学期至少学三门课才符合联邦移民条例）。
Ø 有读PHD打算的学生可以选择论文。
申请要点
Ø GPA:无最低分数要求。最好在3.5以上。
Ø TOEFL：100， ILTS:7.5
Ø GRE必须提交，无最低分数要求。
Master of Science in Engineering in Systems Engineering
“Smart” buildings and highways. Environmental monitoring. Sensor networks. Hybrid systems. The MSE Program in Systems Engineering (SE), grounded in the intersection of electrical and systems engineering, is best positioned to give students the in-depth theoretical foundation and interdisciplinary skills required by the growing complexity of technological systems. Our flexible curriculum allows you to tailor your studies to your personal interests and goals, from signal processing, optimization, simulation, control and cybernetics to complex adaptive systems, stochastic processes and decision sciences.
Course Requirements
Students must complete 10 course units as outlined in the M.S.E. in Systems Engineering Course Planning Guide (CPG):
MSE Systems Engineering Degree Requirements
CATEGORY A: SE FOUNDATION 5 Course Units
- Five (5) course units are required within the three areas below.
- Students must select at least one course unit within each of the three areas.
- Data Science
System ModelingESE 5140 Graph Neural Networks ESE 5280 Estimation & Detection ESE 5380 Machine Learning for Time-Series Data ESE 5390 Hardware/Software Co-Design for Machine Learning (Note: This course requires CIS 2400 or a similar course as a prerequisite and may not be suitable for some students) ESE 5420 Statistics for Machine Learning ESE 5460 Deep Learning ESE 6450 Deep Generative Models ESE 6500 Learning in Robotics CIS 5190 or 5200 Machine Learning
Systems Design & OptimizationESE 5000 Linear System Theory ESE 5030 Simulation Modeling and Analysis ESE 5070 Networks and Protocols ESE 5310 Digital Signal Processing ESE 6650 Datacenter Architecture ENM 5310 Data-driven Modeling and Probabilistic Scientific Computing ESE 5060 Intro to Optimization Theory ESE 5050 Control Systems ESE 5430 Human Systems Engineering ESE 6050 Convex Optimization ESE 6190 Model Predictive Control
CATEGORY B: ESE ELECTIVE 1 Course Unit
- One (1) course unit from any 5000 or 6000 level ESE course.
CATEGORY C: TECHNICAL ELECTIVES 2 Course Units
- Two (2) course units from graduate-level offerings within: ESE, CIS, CIT*, IPD, MEAM, MSE, EAS**, or ENM.
CATEGORY D: APPLICATION AREA 2 Course Units
- Two (2) course units from graduate-level offerings from ONE of the Application Areas below.
- None of these courses may duplicate topics studied in Categories A, B, or C.
- If a course of interest is not listed in the Application Area, the student may send a completed petition (with relevant syllabus attached) to the Master’s Coordinator at least a week before Course Selection Period ends.
- 擅长申请：
- 研究生
- 擅长方案：
- 保研留学双保险
