Master of Engineering (MEng)

Duke’s Master of Engineering (MEng) in Electrical and Computer Engineering offers its students an industry-focused alternative to a traditional master of science program, with a curriculum of eight graduate courses in electrical and computer engineering and two business and management courses.

As a Duke ECE grad student, the most valuable part was close interaction with faculty. I not only took focused classes, but also received one-on-one insights.

Jonathan BuieJonathan Buie
Software Engineer, Intel

In the Master of Engineering program, you take specialized technical classes and a core of business leadership and management courses, with a required internship or a project completing the degree. Our relationships with internationally respected biotech firms in nearby Research Triangle Park may provide unique opportunities for internships.

The program offers students a deeper understanding of technology and develops the business leadership and management expertise needed to succeed in a career in industry.

Duke ECE faculty teach Master of Engineering students in these fields:

Degree requirements, detailed below, include:

  • 30 course credits
  • 1 Seminar
  • Required Internship

Application deadlines:

  • For fall admission: January 15 (Round 1), March 15 (Round 2), June 1 (Round 3–US citizens and permanent residents)

 

How to Apply

 

The Duke Master of Engineering in Electrical and Computer Engineering

The Master of Engineering in Electrical and Computer Engineering is a 30-credit degree distributed as follows:

  • Core Industry Preparation Courses (6 credits)
  • Departmental/Disciplinary or Cross Disciplinary Requirements (15 credits)
  • Technical Electives in a Concentration Area (9 credits)
  • Internship, Project or Equivalent (0 credits)

See important curricular notes.

Core Industry Preparation Course Requirements (6 credits)

The Core Industry Preparation Courses are:

  • MENG 540: Management of High Tech Industries
  • MENG 570: Business Fundamentals for Engineers

Departmental/Disciplinary or Cross Disciplinary Requirements (15 credits)

Required coursework includes:

  • Technical courses (6 credits): Choose two courses from ECE or other approved technical areas.
  • Technical electives (9 credits): With the approval of the student's advisor and the DGS, choose any three elective courses.

Technical Electives in a Concentration Area (9 credits)

Choose three (3) courses from any one (1) discipline listed in sections below. Generally, all three courses should be from one discipline. A custom course plan may also be developed with the approval of the student's advisor.

Computer Engineering

Our concentration area in computer engineering is especially geared toward preparing you for a job in the technology industry.

We have courses designed to give you deep technical knowledge and experience in

  • Software Development

Our highly dedicated and experienced computer engineering faculty includes leading researchers who literally wrote the book on programming.

Our curriculum is designed for students whose undergraduate degrees may not be in engineering or computer science. We’ll make you a serious programmer.

Courses

Key Courses

  • ECE 551: Programming, Data Structures, and Algorithms in C++
  • ECE 550: Fundamentals of Computer Systems and Engineering
  • ECE 651: Software Engineering
  • ECE 650: Systems Programming and Engineering

Other Notable Courses

  • ECE 553: Compiler Construction
  • ECE 555: Probability for Electrical and Computer Engineers.
  • ECE 558: Computer Networks and Distributed Systems
  • ECE 590: Mobile Application Development
  • ECE 590: Performance, Optimization, and Parallelism
  • ECE 590: Enterprise Storage Architecture
  • ECE 563: Cloud Computing
  • ECE 590: Engineering Robust Server Software
  • Hardware Design/Computer Architecture

Duke ECE master's students learn in the classroom and the lab from faculty working on new, resilient hardware architectures for emerging platforms, spanning the gamut from major datacenters to personal mobile devices.

Several faculty members working hardware design have industry experience, including holding visiting research positions at Microsoft Research and Intel Corp.

Courses

Key Courses

  • ECE 550: Fundamentals of Computer Systems and Engineering
  • ECE 552: Advanced Computer Architecture I
  • ECE 559: Advanced Digital Design
  • ECE 539: CMOS VLSI Design Methodologies

Other Notable Courses

  • ECE 555: Probability for Electrical and Computer Engineers.
  • ECE xxx: Datacenter Computing
  • ECE xxx: Energy Efficient Computing
  • ECE 652: Advanced Computer Architecture II
  • ECE 590: Enterprise Storage Architecture
  • ECE 554: Fault Tolerant and Testable Computer Systems
  • ECE 526: Semiconductor Devices for Integrated Circuits.
  • ECE 532: Analog Integrated Circuit Design.
  • ECE 538: VLSI System Testing 

Micro-Nano Systems

Micro-nano systems have the potential to address many of the grand challenges currently facing society, including improving healthcare by engineering better diagnostic tools, securing the homeland by creating better chemical and gas sensors, and reducing the cost of renewable energy sources by increasing the efficiency of solar energy conversion.

Courses

Key Courses

  • ECE 526: Semiconductor Devices for Integrated Circuits
  • ECE 528: Integrated Circuit Engineering
  • ECE 529: Digital Integrated Circuits
  • ECE 539: CMOS VLSI Design Methodologies

Other Notable Courses

  • ECE 511: Foundations of Nanoscale Science and Technology
  • ECE 521: Quantum Mechanics
  • ECE 531: Advanced Topics in ECE*
  • ECE 532: Analog Integrated Circuit Design
  • ECE 537: Radiofrequency (RF) Transceiver Design
  • ECE 590: Advanced Topics in ECE: Biochip Engineering; Micro Mechanical Systems; Advanced Heterojunction Electronics
  • ECE 631: Analog and RF Integrated Circuit Design, Fabrication, and Test* 

*(This course is 1 credit per semester so 3 total credits should be taken in order for it to count as one of the three ECE electives)

Photonics

The ECE department's Photonics concentration is focused on the application of optical and optoelectronic technologies in information science. Photonic applications include information transmission on fiber and free space networks, data storage on disks and volume media, visible and infrared imaging systems, and displays.

Courses

Key Courses

  • ECE 521: Quantum Mechanics
  • ECE 541: Advanced Photonics
  • ECE 545: Nanophotonics
  • ECE 546: Optoelectronic Devices

Other Notable Courses

  • ECE 523: Quantum Information Science
  • ECE 590: Guided Wave Optics
  • ECE 722: Quantum Electronics 

Sensing and Waves

Duke ECE has a strong experimental and theoretical research presence in novel and structured metamaterials, surface science, electromagnetic and acoustic waves, quantum sciences, imaging systems and communication systems. 

Courses

Key Courses

  • ECE 571: Electromagnetic Theory
  • ECE 572: Electromagnetic Communication Systems
  • ECE 573: Optical Communication Systems
  • ECE 574: Waves in Matter

Other Notable Courses

  • ECE 575: Microwave Electronic Circuits
  • ECE 577: Computational Electromagnetics
  • ECE 578S: Inverse Problems in Electromagnetics and Acoustics
  • ECE 675: Optical Imaging and Spectroscopy
  • ECE 676: Lens Design

Signal Processing and Communications (including Big Data and Robotics)

Duke ECE has a strong experimental and theoretical research presence in novel and structured metamaterials, surface science, electromagnetic and acoustic waves, quantum sciences, imaging systems and communication systems. 

Our concentration area in Signal Processing and Communications is designed to give you deep technical knowledge and experience in

  • Big Data Analysis

Our courses in data analysis prepare you for a role in the fast-emerging field of Big Data, through which many of the most important scientific and technological advances of the next several decades will flow.

Our focus on data analysis provides master's students with the tools to manage and interpret large amounts of data through a thorough grounding in the mathematical foundations of Big Data, training in practical programming, and instruction in machine learning, statistics and information theory.

Courses

Key Courses

  • ECE 590: Vector Space Methods With Applications
  • ECE 581: Random Signals and Noise
  • ECE 551: Programming, Data Structures, and Algorithms in C++
  • ECE 681: Pattern Classification and Recognition Technology.
  • STA 561: Probabilistic Machine Learning

Other Notable Courses

  • ECE 582: Digital Signal Processing
  • ECE 590: Textual Data Acquisition and Analysis
  • ECE 590: Uncertainty Analysis [new Spring 2016]
  • STA 601: Bayesian Methods and Modern Statistics
  • ECE 587: Information Theory
  • Robotics

Our Robotics Group trains students in the Duke ECE master's degree programs for work in the design of unmanned aerial vehicles, intelligent vehicles, sensor networks, motion planning and controls, and cyber-physical systems.

Courses

Key Courses

  • ME 555/ECE 590: Introduction to Robotics and Automation
  • ME 627: Linear Systems Theory
  • ME 555: Systems Engineering
  • ME 555/ECE 590: Human-Robot Interaction
  • ME 555/ECE 590: Advanced Robot System Design

Additional Courses

  • COMPSCI 527: Computer Vision
  • COMPSCI 570: Artificial Intelligence
  • COMPSCI 571D: Machine Learning
  • COMPSCI 590: Advanced Topics in CS: Algorithmic Aspects of Machine Learning
  • ME 545: Robot Control and Automation
  • ME 546: Intelligent Systems
  • ME 555: Intelligent Sensors
  • ME 524: Introduction to the Finite Element Method
  • ME 542: Modern Control and Dynamic Systems
  • STA 611: Intro to Mathematical Statistics
  • STA 561: Probabilistic Machine Learning
  • CEE 625(210)/ME 541: Intermediate Dynamics
  • CEE/ME 648: Multivariable Control
  • CEE 690: System Identification
  • ECE 551: Programming, Data Structures, and Algorithms in C++
  • ECE 555: Probability for Electrical and Computer Engineers
  • ECE 581: Random Signals and Noise
  • ECE 590: Vector Space Methods With Applications
  • ECE 681: Pattern Classification and Recognition Technology
  • STA 611: Intro to Mathematical Statistics
  • STA 561: Probabilistic Machine Learning
  • STA 643: Modern Design of Experiments
  • STA 641 Statistical Learning and Bayesian Nonparametrics
  • STA 621 Applied Stochastic Processes 

Sample Degree Plans

Robotics is a broad discipline, and there are a variety of sub-specializations that a Master’s student can choose from. Here we give two example degree plans for robot design and control and intelligent systems sub-specializations. There is some flexibility in each of these plans, and a fourth course each semester should be taken according to your interests and to fulfill degree requirements.

Robot design and control

1st year, Fall semester

  • ME 555/ECE 590: Introduction to Robotics and Automation
  • ME 627: Linear Systems Theory
  • ECE 551: Programming, Data Structures, and Algorithms in C++

1st year, Spring semester

  • ME 542: Modern Control and Dynamic Systems
  • ME 524: Introduction to the Finite Element Method
  • CEE 690: System Identification

2nd year, Fall semester

  • CEE 625(210)/ME 541: Intermediate Dynamics
  • STA 611: Intro to Mathematical Statistics
  • ME 555: Systems Engineering

2nd year, Spring semester

  • ME 555/ECE 590: Advanced Robot System Design
  • CEE/ME 648: Multivariable Control
  • Additional electives, independent study, or MS final project
Intelligent systems

1st year, Fall semester

  • ME 555/ECE 590: Introduction to Robotics and Automation
  • ME 627: Linear Systems Theory
  • ECE 551: Programming, Data Structures, and Algorithms in C++

1st year, Spring semester

  • ME 555/ECE 590: Human-Robot Interaction
  • ECE 555: Probability for Electrical and Computer Engineers or STA 611: Intro to Mathematical Statistics
  • A machine learning, artificial intelligence, or computer vision course

2nd year, Fall semester

  • ME 555: Systems Engineering
  • MATH 561: Scientific Computing
  • A machine learning, artificial intelligence, or computer vision courses

2nd year, Spring semester

  • ME 555/ECE 590: Advanced Robot System Design
  • STA 611: Intro to Mathematical Statistics
  • Additional electives, independent study, or MS final project

Internship, Project or Equivalent Requirement (0 credits)

  • MENG 550: Internship or Applied Research Project
  • MENG 551: Internship/Project Assessment

 

ECE Curriculum Notes

  1. No more than two 300 and 400 level undergraduate coursesand/or ECE 899(399) Independent Study classescan be applied to the MEng degree. Undergraduate courses at the 200 level and lower may be taken, but cannot be used to fulfill MEng degree requirements.
  2. In order to graduate, a student's advisor and ECE DGS must approve the list of courses taken using the Program of Study form.
  3. At least 15 credits must be taken within the Pratt School of Engineering.

 

The Duke Master of Engineering in Photonics and Optical Sciences

MEng students will complete the Core Industry Preparatory Courses and Internship, three technical electives (two of which will be selected from similar areas of concentration) and Departmental Requirements including:

  • An advanced mathematics course
  • Two Optics/Photonics courses
  • One technical optics/photonics course from the BME department and one from the ECE department

Learn more about the Photonics and Optical Sciences MEng

MENG Program Details

Career Services

We provide outstanding career support to our master's students.

As a Duke master's student, you can take advantage of our comprehensive and aggressive career development and job search program. You will receive advice from our Master's Assistant Directors of Career Services who work with the Duke Career Center to coordinate various activities throughout the year.

Our career services include:

  • On-campus recruiting
  • Individual and group career coaching
  • Special networking events such as Career Fairs, Tech Connect, Night with Industry and Alumni Networking Event
  • Resume and cover letter development, interviewing tips, and social media advice

Admissions Profile

The Duke Master of Engineering (MEng) program does not require a minimum GPA or a minimum score on the GRE or TOEFL. The program does not require work experience. Average GRE, TOEFL and grade-point averages of recently admitted Duke MEng applicants were*:

  • GRE Quantitative: 163-169
  • GRE Verbal: 152-161
  • UGPA: 3.4-3.7

* Mid-50% range

Cost of Attendance

Program tuition for the 2017-2018 academic year was $25,860 per semester taken at the university. In general, completion of the 30 required credits over three semesters would result in a total tuition cost of $77,580. Please note that the internship courses do not incur tuition charges. Download 2017-2018 Program Tuition, Fees, and Estimated Expenses.

Financial Aid and Fellowships

Because the Master of Engineering is a professional degree rather than a research degree, most students pay their own tuition costs. Many students take out loans and believe there will be an excellent return on investment when they get out into the work force.

Limited financial aid is available to highly qualified candidates through academic scholarships with an emphasis on increasing diversity within the program.

Detailed Financial Aid Info

Diversity Scholarships

Underrepresented minorities may receive up to 50 percent per year in tuition scholarship through our Diversity Scholarships. 

Externally Funded Scholarships

We also offer support to recipients of select competitively externally funded scholarships, such as

  • National Science Foundation (NSF) Fellowships
  • Fulbright Scholar Program

DoD SMART Scholarship Program

For US Citizens only – The Science, Mathematics And Research for Transformation (SMART) Scholarship for Service Program has been established by the Department of Defense (DoD) to support undergraduate and graduate students pursuing degrees in Science, Technology, Engineering and Mathematics (STEM) disciplines. The program aims to increase the number of civilian scientists and engineers working at DoD laboratories.  Read more...

Veteran's Benefits

Duke University offers information for veterans who are applying for VA benefits, including the Yellow Ribbon Program.

Stafford Loan Program

US citizens and eligible non-citizens may borrow through the Federal Stafford Loan Program. Applicants for assistance through this program must file a Free Application for Federal Student Aid (FAFSA), which may be completed online at https://fafsa.ed.gov/. When completing the online form, students will be asked for Duke’s school code; it is 002920.

Maximum eligibility under the Stafford Unsubsidized Loan Program is $20,500 per year. For further information on the FAFSA and the Stafford Loan Program, please call (800) 433-3243.

International applicants are not eligible for Federal loans; however, many international students take out loans in their home countries, and some US banks may offer loans to international students for study in the United States. Duke University maintains information on lenders for citizens, permanent residents, and non-US citizens.

On-Campus Work

While enrolled in the program, many students work in a variety of places, such as campus libraries and various departments within Duke University. Teaching assistantships are available in various departments, and some departments have research assistantships as well.

These positions are paid an hourly rate, and most students work between 10 to 20 hours per week. Positions are generally posted and filled just a week or two before classes begin each semester.

External Funding Opportunities

Browse our extensive list of potential external funding opportunities on our dedicated website for MEng students.