Master’s Study Tracks
Customize your degree to fit your career goals
When planning your Master of Science (MS) or Master of Engineering (MEng) degree, choose from five study tracks—or design your own!
Choose Your Study Track
Advanced Materials
Dive into the limitless world of materials innovation, from aerospace alloys, to biomedical implants and flexible electronics. This track empowers you to design and engineer novel materials that solve real-world challenges across industries like energy, medicine, and technology.
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- Masters Cornerstone (3 credits)
- Research design, literature review, project management, science communication
- Statistics or programming course (3 credits)
- Concentration courses:
- Select 3 materials science course from the same focal area (9 credits)
Select 2 MEMS Course from any focal area (6 credits)
- Select 3 materials science course from the same focal area (9 credits)
- Project course or independent study for research credit (3 credits)
- 2 Technical elective course (6 credits)
Total: 30 credits
Statistics or programming course options*
- COMPSCI 526 Data Science (T Songdechakrraiwut)
- STA 521L: Predictive Modeling and Statistical Learning (Banks/Jiang)
- STA 522L: Study Design: Design of Surveys and Causal Studies (Reiter)
- STA 523L: Programming for Statistical Science (Rundel)
- STA 611: Introduction to Mathematical Statistics (Banks)
- ME 555-09/CEE 690 Data Science and machine learning for applied science and engineering, (Carlson or Holt)
- *If a student has sufficient programming or stats undergraduate course experience, they may waive this requirement and take an additional ML or materials course instead.
List of Materials Science Courses
Focal areas
Soft matter/Biomaterials
- ME 513: Nanobiomechanics, Marszalek
- ME 514: Theoretical and Applied Polymer Science, Zauscher
- ME 519: Molecular Modeling of Soft Materials, Arya
- ME 555: Intermediate Polymer Physics, Rubinstein, offered occasionally
- ME 563: Fundamentals of Soft Matter, Rubinstein/ Zauscher
- ME 564: Introduction to Polymer Physics, Rubinstein
- CHEM 590 Polymer Synthesis, Becker
- ME 555: Intro to Rheology, Keshavarz
- ME 555: Molecular Modeling of Soft Materials
- ME 511: Computational Materials Science, Blum
- PHYS 760: Mathematical Methods in Physics
- MATH 577(229): Mathematical Modeling
Energy materials
- ME516: Thin-Film Photovoltaic Technology, Mitzi
- ME 515: Electronic Materials, Curtarolo
- ME 531 Engineering Thermodynamics
- ME 532 Convective Heat Transfer
- ME 555 Carbon Capture and Utilization
- ME 536 Compressible Fluid Flow
- ME 555 Intro to Rheology
- ME 572 Engineering Acoustics
- ME 631 Intermediate Fluid Dynamics
- MATH 551 Applied Partial Differential Equations
- ME 524 Finite Element Method
- ME 639 Computational Fluid Dynamics and Heat Transfer
Electronic, photonic, and quantum materials
- ME 510: Diffraction and Spectroscopy, Delaire, Offered
- ME 515: Electronic Materials, Curtarolo
- ECE 521: Quantum Mechanics
- ECE 524 Introduction to Solid-State Physics, Brown
- PHYS 516 Quantum Materials, Baranger
- CHEM 544 Statistical Mechanics
- CHEM 548 Solid-State/Materials Chemistry, Liu, offered occasionally
- PHYS 563 Introduction to Statistical Mechanics
Mechanics of Materials
- CEE 520 Continuum Mechanics, Salahshoor
- CEE 521 Elasticity & Viscoelasticity, Brinson, offered bi-annually
- ME 524: Introduction to the Finite Element Method, Dolbow/Guilleminot
- ME 525: Nonlinear Finite Element Analysis: Dolbow/Guilleminot/Scovazzi
- ME 543: Wave Propagation: Xiaoyue
- CEE 622: Fracture Mechanics: Dolbow
- CEE 623: Mechanics of Composite Materials, Salahshoor
- CEE 690: Multiscale Mechanics of Materials, Guilleminot (if needed)
Other options
- ME 555: Materials Synthesis and Processing
- ME 518: Diffraction and Spectroscopy
- ME 511: Computational Materials Science
- ME 555: Carbon Capture and Utilization
- ME 512: Modern Materials, Payne
- ME535: Biomedical Microsystems: Huang
- ME 562: Materials Synthesis and Processing, Mitzi
- ECE 511 Foundations of Nanoscale Science & Technology, Franklin
- ME 711: Nanotechnology Materials Lab, Walters
- ME 555: Numerical Optimization: Aquino
- CEE 622: Uncertainty Quantification in Science & Engineering: Guilleminot
- Masters Cornerstone (3 credits)
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Fall 1 - Master’s Cornerstone
- Statistics or Python Programming
- Materials Focus Course
- Technical Elective
Spring 1 - Materials Focus Course
- Materials Course
- Research independent study
Fall 2 - Materials Focus Course
- Materials Course
- Technical Elective
- Complete research project/poster
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- Industry preparation courses (6 credits)
- Statistics or programming course (3 credits)
- Concentration courses:
- Select 3 Materials Science courses from the same focal area (9 credits)
- Select 2 MEMS courses from any focal area (6 credits)
- Project course, internship, or independent study for research credit (3 credits)
Total: 30 credits
Statistics or programming course options*
- COMPSCI 526 Data Science (T Songdechakrraiwut)
- STA 521L: Predictive Modeling and Statistical Learning (Banks/Jiang)
- STA 522L: Study Design: Design of Surveys and Causal Studies (Reiter)
- STA 523L: Programming for Statistical Science (Rundel)
- STA 611: Introduction to Mathematical Statistics (Banks)
- ME 555-09/CEE 690 Data Science and machine learning for applied science and engineering, (Carlson or Holt)
- *If a student has sufficient programming or stats undergraduate course experience, they may waive this requirement and take an additional ML or materials course instead.
List of Materials Science Courses
Focal areas
Soft matter/Biomaterials
- ME 513: Nanobiomechanics, Marszalek
- ME 514: Theoretical and Applied Polymer Science, Zauscher
- ME 519: Molecular Modeling of Soft Materials, Arya
- ME 555: Intermediate Polymer Physics, Rubinstein, offered occasionally
- ME 563: Fundamentals of Soft Matter, Rubinstein/ Zauscher
- ME 564: Introduction to Polymer Physics, Rubinstein
- CHEM 590 Polymer Synthesis, Becker
- ME 555: Intro to Rheology, Keshavarz
- ME 555: Molecular Modeling of Soft Materials
- ME 511: Computational Materials Science, Blum
- PHYS 760: Mathematical Methods in Physics
- MATH 577(229): Mathematical Modeling
Energy materials
- ME516: Thin-Film Photovoltaic Technology, Mitzi
- ME 515: Electronic Materials, Curtarolo
- ME 531 Engineering Thermodynamics
- ME 532 Convective Heat Transfer
- ME 555 Carbon Capture and Utilization
- ME 536 Compressible Fluid Flow
- ME 555 Intro to Rheology
- ME 572 Engineering Acoustics
- ME 631 Intermediate Fluid Dynamics
- MATH 551 Applied Partial Differential Equations
- ME 524 Finite Element Method
- ME 639 Computational Fluid Dynamics and Heat Transfer
Electronic, photonic, and quantum materials
- ME 510: Diffraction and Spectroscopy, Delaire, Offered
- ME 515: Electronic Materials, Curtarolo
- ECE 521: Quantum Mechanics
- ECE 524 Introduction to Solid-State Physics, Brown
- PHYS 516 Quantum Materials, Baranger
- CHEM 544 Statistical Mechanics
- CHEM 548 Solid-State/Materials Chemistry, Liu, offered occasionally
- PHYS 563 Introduction to Statistical Mechanics
Mechanics of Materials
- CEE 520 Continuum Mechanics, Salahshoor
- CEE 521 Elasticity & Viscoelasticity, Brinson, offered bi-annually
- ME 524: Introduction to the Finite Element Method, Dolbow/Guilleminot
- ME 525: Nonlinear Finite Element Analysis: Dolbow/Guilleminot/Scovazzi
- ME 543: Wave Propagation: Xiaoyue
- CEE 622: Fracture Mechanics: Dolbow
- CEE 623: Mechanics of Composite Materials, Salahshoor
- CEE 690: Multiscale Mechanics of Materials, Guilleminot (if needed)
Other options
- ME 555: Materials Synthesis and Processing
- ME 518: Diffraction and Spectroscopy
- ME 511: Computational Materials Science
- ME 555: Carbon Capture and Utilization
- ME 512: Modern Materials, Payne
- ME535: Biomedical Microsystems: Huang
- ME 562: Materials Synthesis and Processing, Mitzi
- ECE 511 Foundations of Nanoscale Science & Technology, Franklin
- ME 711: Nanotechnology Materials Lab, Walters
- ME 555: Numerical Optimization: Aquino
- CEE 622: Uncertainty Quantification in Science & Engineering: Guilleminot
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Fall 1 - MEng 570: Business Fundamentals
- Statistics or Python Programming
- Materials Focus Course
Spring 1 - MEng 540: Leadership & Management
- Materials Focus Course
- Materials Course
Summer - MEng 550: Internship or Project
Fall 2 - Materials Focus Course
- Materials Course
- MEng 551: Internship Assessment
- Technical Elective
Aerospace
Launch your career in flight and space with deep expertise in aerodynamics, fluid mechanics, and structural dynamics. This track equips you to tackle the toughest engineering challenges in aviation and beyond.
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- Participants must select a focus area:
- Structures & Dynamics
- Aerodynamics & Acoustics
- Complete four technical courses:
- Each focus area has two (2) required core courses (substitutions may be approved in advance by the Certificate Director)
- Two additional courses must be taken, with at least one (1) in another focus area
- Note: only one (1) Mathematical and Computational Methods course can be counted toward the four required courses
- Research thesis poster or research thesis
- Cornerstone Course
- 5 technical electives (500+) from the department or across Pratt, could include research independent studies
Total: 30 credits
Structures & Dynamics Courses
- ME 544: Advanced Mechanical Vibrations (Preapproved Core Course)
- ME 524 / CE 530: Introduction to Finite Element Methods (Preapproved Core Course)
- ME 527: Buckling of Engineering Structures
- CEE 541: Structural Dynamics
- ME 541: Intermediate Dynamics
- ME 742: Nonlinear Mechanical Vibrations
- CEE 629: System Identification
Aerodynamics & Acoustics Courses
- ME 571: Aerodynamics (Preapproved Core Course)
- ME 572: Engineering Acoustics (Preapproved Core Course)
- ME 672: Unsteady Aerodynamics
- ME 671: Advanced Aerodynamics
- ME 555: Advanced Acoustics
- ME 775: Aeroelasticity
Mathematical and Computational Methods Courses
- MATH 551: Applied Partial Differential Equations & Complex Variables (Fall)
- MATH 577: Mathematical Modeling (Spring)
- ME 639: Computational Fluid Mechanics and Heat Transfer (Fall)
- ME 524 / CE 530: Finite Element Method (Fall)
- MATH 561: Numerical Linear Algebra, Optimization & Monte Carlo Simulation (Fall)
- COMPSCI 520: Numerical Analysis (Spring)
- MATH 563: Applied Computational Analysis (Spring)
- Participants must select a focus area:
-
- Participants must select a focus area:
- Structures & Dynamics
- Aerodynamics & Acoustics
- Complete four technical courses:
- Each focus area has two (2) required core courses (substitutions may be approved in advance by the Certificate Director)
- Two additional courses must be taken, with at least one (1) in another focus area
- Note: only one (1) Mathematical and Computational Methods course can be counted toward the four required courses
- Research thesis poster or research thesis
- Cornerstone Course
- 2 businesses courses (MEng 540 and MEng 570)
- 1 internship
- 3 technical electives (500+) from the department or across Pratt
Total: 30 credits
Structures & Dynamics Courses
- ME 544: Advanced Mechanical Vibrations (Preapproved Core Course)
- ME 524 / CE 530: Introduction to Finite Element Methods (Preapproved Core Course)
- ME 527: Buckling of Engineering Structures
- CEE 541: Structural Dynamics
- ME 541: Intermediate Dynamics
- ME 742: Nonlinear Mechanical Vibrations
- CEE 629: System Identification
Aerodynamics & Acoustics Courses
- ME 571: Aerodynamics (Preapproved Core Course)
- ME 572: Engineering Acoustics (Preapproved Core Course)
- ME 672: Unsteady Aerodynamics
- ME 671: Advanced Aerodynamics
- ME 555: Advanced Acoustics
- ME 775: Aeroelasticity
Mathematical and Computational Methods Courses
- MATH 551: Applied Partial Differential Equations & Complex Variables (Fall)
- MATH 577: Mathematical Modeling (Spring)
- ME 639: Computational Fluid Mechanics and Heat Transfer (Fall)
- ME 524 / CE 530: Finite Element Method (Fall)
- MATH 561: Numerical Linear Algebra, Optimization & Monte Carlo Simulation (Fall)
- COMPSCI 520: Numerical Analysis (Spring)
- MATH 563: Applied Computational Analysis (Spring)
- Participants must select a focus area:
AI for Materials
Be at the cutting edge where artificial intelligence meets materials science. Use machine learning and advanced simulations to accelerate discoveries and transform how new materials are designed.
This track is a more flexible version of the AI + Materials Master of Engineering. Students interested in this track should also consider looking into the AI + Materials MEng program.
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- “Masters Cornerstone” (3 credits, 1 course)
- Research design, literature review, project management, science communication
- Statistics or programming course (3 credits, 1 course)
- Concentration courses (6 courses):
- 1 Intro to ML (3 credits)
- 1 Advanced ML course (3 credits)
- 2 Materials courses (6 credits)
- 2 Computational/AI Materials courses (6 credits)
- AI + Materials research independent study (3 credits, 1 course)
- 1 Elective course
Total: 30 credits
Statistics or Programming Course Options*
- COMPSCI 526: Data Science (T. Songdechakrraiwut)
- STA 521L: Predictive Modeling and Statistical Learning (Banks/Jiang)
- STA 522L: Study Design: Design of Surveys and Causal Studies (Reiter)
- STA 523L: Programming for Statistical Science (Rundel)
- STA 611: Introduction to Mathematical Statistics (Banks)
*If a student has sufficient programming or stats undergraduate course experience, they may waive this requirement and take an additional ML or materials course instead.
Programming and Intro to ML Course Options
- ME 555-09 / CEE 690: Data Science and Machine Learning for Applied Science and Engineering (Carlson or Holt)
- CS 570: Artificial Intelligence (Conitzer or Parr)
- ECE 580: Introduction to Machine Learning (Collins, X. Li, or Tantum)
- ECE 682D / STA 561D: Probabilistic Machine Learning (E. Laber)
Advanced ML Course Options
- ECE 685D / COMPSCI 675D: Introduction to Deep Learning (V. Tarokh)
- COMPSCI 527: Introduction to Computer Vision (C. Tomasi)
- ECE 590: Advanced Deep Learning (V. Tarokh)
- ECE 590: Computer Engineering ML and Deep Neural Nets (Y. Chen / H. Li)
- COMPSCI 570: Artificial Intelligence (Parr)
- COMPSCI 661
- COMPSCI 671D: Theory and Algorithms for Machine Learning (C. Rudin) (requires prior experience with Python, probability, and linear algebra)
- COMPSCI 572: Introduction to Natural Language Processing (M. Agrawal)
Materials Course Options
- CEE 520: Continuum Mechanics (CMSC faculty)
- CEE 521: Elasticity (Brinson, offered occasionally)
- CHEM 548: Solid-State/Materials Chemistry (Liu, offered occasionally)
- CHEM 590: Polymer Synthesis (Becker, Spring)
- ECE 524: Introduction to Solid-State Physics (Brown)
- ECE 511: Foundations of Nanoscale Science & Technology (Franklin)
- ME 510: Diffraction and Spectroscopy (Delaire)
- ME 512: Modern Materials (Payne)
- ME 514: Theoretical and Applied Polymer Science (Zauscher)
- ME 515: Electronic Materials (Curtarolo
- ME 519: Molecular Modeling of Soft Materials (Arya)
- ME 555: Intermediate Polymer Physics (Rubinstein, offered occasionally)
- ME 555: Intro to Rheology (Keshavarz)
- ME 562: Materials Synthesis and Processing (Mitzi)
- ME 563: Fundamentals of Soft Matter (Rubinstein/Zauscher)
- ME 564: Introduction to Polymer Physics (Rubinstein)
- PHYS 516: Quantum Materials (Baranger)
Computational Materials Courses (any of the below can be taken prior to Intro ML)
- ME 555: Numerical Optimization (Aquino)
- CEE 628: Uncertainty Quantification (Guilleminot)
- ME 511: Computational Materials Science (Blum)
- ME 524: Introduction to the Finite Element Method (CMSC faculty)
- ME 525: Nonlinear Finite Element Analysis (CMSC faculty)
- ME 519: Molecular Modeling of Soft Materials (Arya)
- ME 555: Multiscale Methods (Guilleminot)
AI – Materials Courses
- ME 582: Data and Materials Science Applications (Various)
- ME 555: Scientific Computing, Simulation and ML (Aquino)
Project (MS Students) or Internship (MEng Students)
- ME 555: Fall/Spring Data and Materials Science Capstone (3 units)
- Other research project course or independent study course related to machine learning and materials science (requires pre-approval)
- Internship or external research experience related to machine learning and materials science (approved as a 3–4 unit course by DGS)
- “Masters Cornerstone” (3 credits, 1 course)
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Fall 1 - Intro ML / Python (“ML for Engineers”)
- Master’s Cornerstone Course
- Materials or Computational Materials Course
- Statistics or Python Programming (optional)
Spring 1 - Advanced ML Course
- AI + Materials Course (ME 582)
- AI + Materials Project or Independent Study
- Scientific Elective
Fall 2 - Advanced ML Course
- Materials or Computational Materials Course
- Materials or Computational Materials Course
- Complete project / independent study / Poster
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Note: this track is a more flexible version of the program pursued in the AI + Materials Master of Engineering. Students interested in this track may also want to consider checking out the AI + Materials MEng to see which program better suits their needs.
- Industry preparation courses (6 credits, 2 courses; internship summer course +1 credit)
- Statistics or programming course (3 credits, 1 course)
- Concentration courses (6 courses):
- 1 Intro to ML (3 credits)
- 1 Advanced ML course (3 credits)
- 2 Materials courses (6 credits)
- 2 Computational/AI Materials courses (6 credits)
- AI + Materials research course or Elective (3 credits, 1 course)
Total: 30 credits
Statistics or Programming Course Options*
- COMPSCI 526: Data Science (T. Songdechakrraiwut)
- STA 521L: Predictive Modeling and Statistical Learning (Banks/Jiang)
- STA 522L: Study Design: Design of Surveys and Causal Studies (Reiter)
- STA 523L: Programming for Statistical Science (Rundel)
- STA 611: Introduction to Mathematical Statistics (Banks)
*If a student has sufficient programming or stats undergraduate course experience, they may waive this requirement and take an additional ML or materials course instead.
Programming and Intro to ML Course Options
- ME 555-09 / CEE 690: Data Science and Machine Learning for Applied Science and Engineering (Carlson or Holt)
- CS 570: Artificial Intelligence (Conitzer or Parr)
- ECE 580: Introduction to Machine Learning (Collins, X. Li, or Tantum)
- ECE 682D / STA 561D: Probabilistic Machine Learning (E. Laber)
Advanced ML Course Options
- ECE 685D / COMPSCI 675D: Introduction to Deep Learning (V. Tarokh)
- COMPSCI 527: Introduction to Computer Vision (C. Tomasi)
- ECE 590: Advanced Deep Learning (V. Tarokh)
- ECE 590: Computer Engineering ML and Deep Neural Nets (Y. Chen / H. Li)
- COMPSCI 570: Artificial Intelligence (Parr)
- COMPSCI 661
- COMPSCI 671D: Theory and Algorithms for Machine Learning (C. Rudin) (requires prior experience with Python, probability, and linear algebra)
- COMPSCI 572: Introduction to Natural Language Processing (M. Agrawal)
Materials Course Options
- CEE 520: Continuum Mechanics (CMSC faculty)
- CEE 521: Elasticity (Brinson, offered occasionally)
- CHEM 548: Solid-State/Materials Chemistry (Liu, offered occasionally)
- CHEM 590: Polymer Synthesis (Becker)
- ECE 524: Introduction to Solid-State Physics (Brown)
- ECE 511: Foundations of Nanoscale Science & Technology (Franklin)
- ME 510: Diffraction and Spectroscopy (Delaire)
- ME 512: Modern Materials (Payne)
- ME 514: Theoretical and Applied Polymer Science (Zauscher)
- ME 515: Electronic Materials (Curtarolo)
- ME 519: Molecular Modeling of Soft Materials (Arya)
- ME 555: Intermediate Polymer Physics (Rubinstein, offered occasionally)
- ME 555: Intro to Rheology (Keshavarz)
- ME 562: Materials Synthesis and Processing (Mitzi)
- ME 563: Fundamentals of Soft Matter (Rubinstein/Zauscher)
- ME 564: Introduction to Polymer Physics (Rubinstein)
- PHYS 516: Quantum Materials (Baranger)
Computational Materials Courses (any of the below can be taken prior to Intro ML)
- ME 555: Numerical Optimization (Aquino)
- CEE 628: Uncertainty Quantification (Guilleminot)
- ME 511: Computational Materials Science (Blum)
- ME 524: Introduction to the Finite Element Method (CMSC faculty)
- ME 525: Nonlinear Finite Element Analysis (CMSC faculty)
- ME 519: Molecular Modeling of Soft Materials (Arya)
- ME 555: Multiscale Methods (Guilleminot)
AI – Materials Courses
- ME 582: Data and Materials Science Applications (Various)
- ME 555: Scientific Computing, Simulation and ML (Aquino)
Project (MS Students) or Internship (MEng Students)
- ME 555: Fall/Spring Data and Materials Science Capstone (3 units)
- Other research project course or independent study course related to machine learning and materials science (requires pre-approval)
- Internship or external research experience related to machine learning and materials science (approved as a 3–4 unit course by DGS)
-
Fall 1 - MEng 570: Business Fundamentals
- Statistics or Python Programming
- Materials Course
Spring 1 - MEng 540: Leadership & Management
- Intro ML
- Materials Course
- Computational Materials Course
Summer - MEng 550: Internship or Project
Fall 2 - Advanced ML Course
- Computational Materials Course
- MEng 551: Internship Assessment
Medical Robotics
Step into the future of medicine with hands-on experience in surgical robots, AI, and advanced control systems. This track prepares you to design life-changing technologies that push the boundaries of healthcare innovation.
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Core Robotics Courses
- Master’s Cornerstone (3 credits)
- ME 555 Introduction to Robotics (fall term only, prerequisite for other courses) (3 credits)
- ME 555 Introduction to Medical Robotics and Surgical Technologies (fall term only, prerequisite for other courses) (3 credits)
Programming / Perception / AI / ML (3 credits)
- ME 555 Introduction to Programming
- CS 527 Computer Vision
- BME 548L Machine Learning in Imaging
- COMPSCI 570 / ECE 590D Artificial Intelligence
Projects in Medical Robotics (3 credits)
- ME 555 Medical Robotics and Surgical Technologies Team Project (spring term only)
- Continuum Robotics (fall term only; likely ME 555)
Dynamics and Controls (6 credits — try to take both Dynamics and Controls)
- ME 555 Data-Driven Dynamical Systems and Control
- ME 541 Intermediate Dynamics
- ME 555 Model Predictive Control
- ME 627 Linear System Theory
Electives (9 credits)
Suggested electives:- Any of the above courses not already taken (credit will not double count)
- ME 555 Robotic Manipulation
- ME 524 Introduction to the Finite Element Method
- ME 555 Numerical Optimization
- BME 671 Signal Processing and Applied Mathematics
- ME 555 Scientific Computing, Simulation & ML
- BME 590 Advanced Wearable System Design
- BME 590 AI and Biocomputation
- ME 555 Advanced Robotics
Total: 30 credits
-
Fall 1 - ME 555 Introduction to Robotics
- ME 555 Introduction to Medical Robotics and Surgical Technologies
- Master’s Cornerstone
Spring 1 - Programming / Perception / AI / ML course
- ME 555 Medical Robotics and Surgical Technologies Team Project
- Dynamics course
- Controls course
Fall 2 - Elective
- Elective
- Elective
-
Industry Preparation Courses (6 credits)
Core Robotics Courses
- ME 555 Introduction to Robotics (fall term only; prerequisite for other courses) (3 credits)
- ME 555 Introduction to Medical Robotics and Surgical Technologies (fall term only; prerequisite for other courses) (3 credits)
Programming / Perception / AI / ML (3 credits)
- ME 555 Introduction to Programming
- CS 527 Computer Vision
- BME 548L Machine Learning in Imaging
- COMPSCI 570 / ECE 590D Artificial Intelligence
Projects in Medical Robotics (3 credits)
- ME 555 Medical Robotics and Surgical Technologies Team Project (spring term only)
- Continuum Robotics (fall term only; likely ME 555)
Dynamics and Controls (6 credits)
- ME 555 Data-Driven Dynamical Systems and Control
- ME 541 Intermediate Dynamics
- ME 555 Model Predictive Control
- ME 627 Linear System Theory
Electives (6 credits)
(Flexibility or structured options TBD)- Any of the above courses not already taken (credit will not double count)
- ME 555 Robotic Manipulation
- ME 524 Introduction to the Finite Element Method
- ME 555 Numerical Optimization
- BME 671 Signal Processing and Applied Mathematics
- ME 555 Scientific Computing, Simulation & ML
- BME 590 Advanced Wearable System Design
- BME 590 AI and Biocomputation
Total: 30 credits
-
Fall 1 - ME 555 Introduction to Robotics
- ME 555 Introduction to Medical Robotics and Surgical Technologies
- Industry Preparation Course 1
Spring 1 - Programming / Perception / AI / ML course
- ME 555 Medical Robotics and Surgical Technologies Team Project
- Dynamics course
- Industry Preparation Course 2
Fall 2 - Controls course
- Elective
- Elective
Robotics
Get ready to lead the robotics revolution with a blend of hands-on technical training and real-world experience. This track will uniquely prepare to you to shape autonomous systems across industries with insight, responsibility, and impact.
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- Robotics Core — 2 courses (of Computer Vision, Intro to Programming, Intro to Robotics)
- Machine Learning course
- Controls & Dynamics course
- An Ethics course
- 2 Technical Robotics electives (see certificate)
- 2 technical electives (500+) from the department or across Pratt, could include research independent studies
- Cornerstone or Projects in Medical Robotics
- Research thesis poster or research thesis
-
Fall 1 - ME 555 Cornerstone/Experimental Design
- ME 555 Introduction to Robotics
- ECE 580 Introduction to Machine Learning
Spring 1 - ME 555 Introduction to Programming
- ME 555 Model Predictie Control
- ME 555 Case Studies of Ethics in Robotics and Automation
- Technical Elective 1
Fall 2 - Robotics Elective 1
- Robotics Elective 2
- Technical Elective 2
-
- Career Preparation Core:
- MENG 540 Management of High-Tech Industries
- MENG 570 Business Fundamentals for Engineers
- MENG 550/551 Industry Internship & Assessment
- 2 Technical Robotics electives (see certificate)
- Robotics Core — 2 courses (of Computer Vision, Intro to Programming, Intro to Robotics)
- Machine Learning course
- Controls & Dynamics course
- An Ethics course
- Cornerstone Project
- 1 internship
- Career Preparation Core:
-
Fall 1 - MENG 540 Management of High-Tech Industries
- ME 555 Introduction to Robotics
- ECE 580 Introduction to Machine Learning
Spring 1 - MENG 570 Business Fundamentals for Engineers
- ME 555 Introduction to Programming
- ME 555 Case Studies of Ethics in Robotics and Automation
- ME 555 Model Predictive Control
Summer - MENG 550 Internship (no tuition charged)
Fall 2 - MENG 551 Internship Assessment
- Rob Elective 1
- Rob Elective 2
- ME 555 Cornerstone
Design Your Own
Have the freedom to create a master’s experience that’s tailored to your passions and career goals, putting you in the driver’s seat of your graduate education.
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Take advantage of all the areas of expertise that the MEMS department has to offer, including structural mechanics, energy and cross-disciplinary opportunities with other schools across Duke.
Students will need to work with the Director of Master’s Studies to tailor this program to their individual needs.
Master’s Contacts
Siobhan Rigby Oca, Director of Master’s Studies
Shauntil Gray, Director of Master’s Studies Assistant