近些年，隨著互聯(lián)網(wǎng)的普及，各個領(lǐng)域都少不了計算機(jī)技術(shù)人才，尤其是在工程領(lǐng)域，急需一批既有計算機(jī)技術(shù)又懂得工程技術(shù)的復(fù)合型人才，為了順應(yīng)時代的需求，麻省理工學(xué)院就開設(shè)了計算機(jī)科學(xué)與工程碩士專業(yè)，下面，就隨小編來看看吧，希望對大家有所幫助：

　　Master of Science Program in Computational Science and Engineering

　　計算科學(xué)與工程(CSE)碩士學(xué)位，前身為設(shè)計與優(yōu)化計算(CDO) SM項(xiàng)目，是一個跨學(xué)科項(xiàng)目，旨在為未來的工程師和科學(xué)家在先進(jìn)的計算方法和應(yīng)用做準(zhǔn)備。該計劃提供了一個強(qiáng)大的基礎(chǔ)計算方法的設(shè)計和操作的復(fù)雜工程和科學(xué)系統(tǒng)。

　　作為一個跨學(xué)科的學(xué)術(shù)項(xiàng)目，CSE SM位于計算科學(xué)與工程中心，但學(xué)生有機(jī)會與研究所的教員一起工作。通過實(shí)踐項(xiàng)目和碩士論文，學(xué)生發(fā)展和應(yīng)用先進(jìn)的計算方法到不同的應(yīng)用范圍，從航空航天到納米技術(shù)，從互聯(lián)網(wǎng)協(xié)議到電信系統(tǒng)設(shè)計。CSE SM畢業(yè)生的就業(yè)機(jī)會包括系統(tǒng)建模、數(shù)值模擬、設(shè)計和優(yōu)化扮演關(guān)鍵角色的公司和研究中心。

　　課程設(shè)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 ? 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)

　　入學(xué)要求：

　　要被錄取為普通研究生，申請人必須從具有可接受地位的學(xué)院、大學(xué)或技術(shù)學(xué)校獲得學(xué)士學(xué)位或同等學(xué)歷。

　　申請材料：

　　三份PDF格式的推薦信;

　　所有曾就讀的學(xué)院/大學(xué)的成績單，以PDF格式直接上載至申請表格;

　　目標(biāo)陳述(限一頁左右);

　　個人簡歷或簡歷，PDF格式上傳;

　　麻省理工學(xué)院研究生申請費(fèi)為75美元。

　　語言要求：

　　雅思學(xué)術(shù)考試成績7分或以上

　　綜上所述，以上講的就是關(guān)于麻省理工學(xué)院計算機(jī)科學(xué)與工程碩士專業(yè)的相關(guān)問題介紹，希望能給各位赴美留學(xué)的學(xué)子們指點(diǎn)迷津。近年來，赴美留學(xué)一直是廣大學(xué)生最熱門的話題，同時，很多學(xué)生對于簽證的辦理、院校的選擇、就業(yè)的前景、學(xué)習(xí)的費(fèi)用等諸多問題困擾不斷，別擔(dān)心，IDP留學(xué)專家可以為你排憂解難，同時，更多關(guān)于赴美留學(xué)的相關(guān)資訊在等著你，絕對讓你“瀏覽”忘返。在此，衷心祝愿各位學(xué)子們能夠順利奔赴自己心目中理想的學(xué)校并且學(xué)業(yè)有成!