MEng in Electrical and Computer Engineering

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 8 graduate courses in electrical and computer engineering and 2 business and management courses. 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.

To complete a Master of Engineering in Electrical and Computer Engineering, students complete

  • Two core business and management courses (6 credits)
    • MEng 540: Management of High-Tech Industries
    • MEng 570: Business Fundamentals for Engineers
  • Three Area of Concentration electives (9 credits)
  • Five departmental courses in disciplinary or cross-disciplinary areas (15 credits)
    • Advisor-approved courses in electrical and computer engineering or other areas of study
  • An internship or project (0 credits)

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Areas of Concentration

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 [New in Spring 2017]

 

  • 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

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

  • Fundamentals of Robotics (offered starting Fall 2017)
  • Robot Decision Making (offered starting Spring 2018)
  • Advanced Robot Systems Design (offered starting Fall 2018)
  • ECE 590.XX: Advanced Topics in ECE: Robot Planning, Perception, and Control

Other Notable Courses

  • ECE 551: Programming, Data Structures, and Algorithms in C++
  • ECE 590.xx: Vector Space Methods With Applications
  • ECE 581: Random Signals and Noise
  • ECE 681: Pattern Classification and Recognition Technology
  • ECE 588: Image and Video Processing
  • STA 561: Machine Learning
  • ME 548(263). Multivariable Control
  • ME 542(230). Modern Control and Dynamic Systems
  • ME 555.XX: Autonomous Systems Engineering
  • ME 555.XX: Human Factors Engineering

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