麻省理工学院计算机科学与工程硕士专业 让你成为一名出色的IT工程师!
近些年，随着互联网的普及，各个领域都少不了计算机技术人才，尤其是在工程领域，急需一批既有计算机技术又懂得工程技术的复合型人才，为了顺应时代的需求，麻省理工学院就开设了计算机科学与工程硕士专业，下面，就随小编来看看吧，希望对大家有所帮助：
Master of Science Program in Computational Science and Engineering
计算科学与工程(CSE)硕士学位，前身为设计与优化计算(CDO) SM项目，是一个跨学科项目，旨在为未来的工程师和科学家在先进的计算方法和应用做准备。该计划提供了一个强大的基础计算方法的设计和操作的复杂工程和科学系统。
作为一个跨学科的学术项目，CSE SM位于计算科学与工程中心，但学生有机会与研究所的教员一起工作。通过实践项目和硕士论文，学生发展和应用先进的计算方法到不同的应用范围，从航空航天到纳米技术，从互联网协议到电信系统设计。CSE SM毕业生的就业机会包括系统建模、数值模拟、设计和优化扮演关键角色的公司和研究中心。
课程设置：
Core Subjects (3 courses / 36 units)
2.096J/6.336J/16.910J Introduction to Numerical Simulation (F)
2.097J/6.339J/16.920J Numerical Methods for Partial Differential Equations (F)
6.255J/15.093J Optimization Methods (F)
6.337J/18.335J Introduction to Numerical Methods (S)
Restricted Electives (2 courses / 24 units)*†
• 1.124[J] / 2.091[J] Software and Computation for Simulation (F)
• 1.125 Architecting & Engineering Software Systems (F)
• 1.545 Atomistic Modeling & Simulations of Materials & Structures (F)
• 1.583 Topology Optimization of Structures (F)
• 1.723 Computational Methods for Flow in Porous Media (F)
• 2.089[J] / 1.128[J] Computational Geometry (S)
• 2.096[J] / 6.336[J] / 16.910[J] Introduction to Numerical Simulation (F)
• 2.097[J] / 6.339[J] / 16.920[J] Numerical Methods for Partial Differential Equations (F)
• 2.098 Introduction to Finite Element Methods for Partial Differential Equations (S)
• 2.111[J] / 8.370[J] / 18.435[J] Quantum Computation (F)
• 2.168 Learning Machines (S)
• 2.29 Numerical Fluid Mechanics (S)
• 3.320 Atomistic Computer Modeling of Materials (F)
• 4.450[J] / 1.575[J] Computational Structural Design and Optimization (F)
• 4.453 Creative Machine Learning for Design (S)
• 6.231 Dynamic Programming and Reinforcement Learning (S)
• 6.251[J] / 15.081[J] Introduction to Mathematical Programming (F)
• 6.252[J] / 15.084[J] Nonlinear Optimization (S)
• 6.255[J] / 15.093[J] Optimization Methods (F)
• 6.256 Algebraic Techniques and Semidefinite Optimization (S)
• 6.265[J] / 15.070[J] Discrete Probability and Stochastic Processes (S)
• 6.337[J] / 18.335[J] Introduction to Numerical Methods (S)
• 6.435 Bayesian Modeling and Inference (S)
• 6.438 Algorithms for Inference (F)
• 6.439[J] / IDS.131[J] Statistics, Computation and Applications (F)
• 6.482 Modeling with Machine Learning: from Algorithms to Applications (S)
• 6.838 Shape Analysis (S)
• 6.860[J] / 9.520[J] Statistical Learning Theory and Applications (F)
• 6.867 Machine Learning (F)
• 6.869 Advances in Computer Vision (S)
• 6.XXX* Optimization for Machine Learning (*Spring 2023: 6.881 until permanent # assigned; Instructor:
9.660 Computational Cognitive Science (F)
• 10.554 [J] / 2.884 [J] Process Data Analytics and Machine Learning (F)
• 10.557 Mixed-integer and Nonconvex Optimization (S)
• 10.637[J] / 5.698[J] Quantum Chemical Simulation (F)
• 12.515 Data and Models (F)
• 12.521 Computational Geophysical Modeling (F)
• 12.620 Classical Mechanics: A Computational Approach (F)
• 12.714 Computational Data Analysis (S)
• 12.805 Data Analysis in Physical Oceanography (S)
• 12.850 Computational Ocean Modeling (S)
• 15.077[J] / IDS.211[J] Statistical Learning and Data Mining (S; Cannot be used if taken Fall 2015 or after & credit also received for 6.867)
• 15.083 Integer Programming and Combinatorial Optimization (S; Sloan bidding process required)
• 15.764[J] / 1.271[J] / IDS.250[J] Theory of Operations Management (S)
• 16.110 Flight Vehicle Aerodynamics (F)
• 16.225[J] / 2.099[J] Computational Mechanics of Materials (S)
• 16.413 Principles of Autonomy and Decision Making (F)
• 16.888[J] / IDS.338[J] EM.428 Multidisciplinary Design Optimization (S)
• 16.930 Advanced Topics in Numerical Methods for Partial Differential Equations (S)
• 16.940 Numerical Methods for Stochastic Modeling & Inference (F)
• 18.336[J] / 6.335[J] Fast Methods for Partial Differential and Integral Equations (F)
• 18.337[J] / 6.338[J] Parallel Computing & Scientific Machine Learning (F)
• 18.369 Mathematical Methods in Nanophotonics (S)
• 22.15 Essential Numerical Methods (F; first ½ of term)
• 22.212 Nuclear Reactor Analysis II (F)
• 22.213 Nuclear Reactor Physics III (S)
• 22.315 Applied Computational Fluid Dynamics and Heat Transfer (S)
入学要求：
要被录取为普通研究生，申请人必须从具有可接受地位的学院、大学或技术学校获得学士学位或同等学历。
申请材料：
三份PDF格式的推荐信;
所有曾就读的学院/大学的成绩单，以PDF格式直接上载至申请表格;
目标陈述(限一页左右);
个人简历或简历，PDF格式上传;
麻省理工学院研究生申请费为75美元。
语言要求：
雅思学术考试成绩7分或以上
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