Engineering Sciences (Robotics) MS

Small robot on floor.
Students working on a robot in lab.
Robot moving a block on a conveyor belt in automation lab.
Video of robot working on jewelry in a UB lab.

Engineering Science MS (Robotics) Program enhances its industrial educational capabilities with VDK1200 - Open Architecture Jewelry Material Removal System.

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About the Program

The Engineering Science MS with a course focus in Robotics is an interdisciplinary program that will train students in robotics and expand knowledge in automation, leading to employment opportunities in automated manufacturing or continued education at the doctoral level.

Areas of training in our courses include robotic integration, motion control development, electromechanical system design, device/sensor development, automation platforms, and Artificial Intelligence (AI).

Internships and Employment

The job market demand for students with a background in robotics and related fields is expected to continue to grow at an increased rate for companies in search of students trained in this area. Faculty members associated with the Engineering Science MS (Robotics) Program actively work with students to help them secure positions upon completion of the program. Typically, a graduate degree is necessary for advancement in this field, and students completing UB's program have an opportunity to practice in industry and directly deploy skills acquired through the program. 

Program Director

Vojislav Kalanovic
1006 Furnas Hall
716-645-1417
vojislav@buffalo.edu

Graduate Coordinator

415 Bonner Hall
engsci@buffalo.edu

Entrance Requirements

We seek to admit students to our masters program with backgrounds or related coursework in electrical, mechanical, computer or any related engineering field, computer science, mathematics, physics or a related physical science field.

We are temporarily suspending the GRE requirement for admission to our Engineering Science MS (Robotics) Program for the Spring 2021 and Fall 2021 entry terms.

Degree Program Specifics

Students will take 10 courses for a total of 30 credits.  

Our flexible packaged curriculum enables students to focus on the core competencies related to that align with their own personal training goals that align to meet the needs of preparing them for their careers.

Course Requirements

Core Courses

  • CSE 568 Robotics Algorithms                                       
  • CSE 573 Computer Vision and Image Processing
  • CSE 574 Intro Machine Learning
  • MAE 507 Engineering Analysis 1 or MAE 567 Vibration and Shock 1*
  • MAE 543 Continuous Control Systems or EAS 596 Robot Control Systems
  • MAE 593 Robotics I

*EAS 502 Probability replacing MAE 507 Engineering Analysis effective Fall 2022

Electives

Students will pick 2-3 electives; 2 electives if doing a 6-credit Master’s Thesis for the culminating experience and 3 courses if opting for the 3-credit Master’s Project.

  • CSE 531 Analysis of Algorithms
  • CSE 546 Reinforcement Learning
  • CSE 555 Intro to Pattern Recognition
  • CSE 668 Advanced Robotics
  • CSE 673 Computational Vision
  • CSE 674 Advanced Machine Learning
  • CSE 676 Deep Learning
  • EAS 595 Fundamentals of AI
  • IE 506 Computer Integrated Manufacturing
  • MAE 502 Special topics: Human Robot Interaction
  • MAE 520 Musculoskeletal Biomechanics
  • MAE 525 Space Dynamics & Control
  • MAE 527 Special Topics: Intelligent Interfaces
  • MAE 544 Digital Control Systems
  • MAE 550 Optimization in Engineering Design
  • MAE 554 Road Vehicle Dynamics
  • MAE 562 Analytical Dynamics
  • MAE 564 Manufacturing Automation
  • MAE 566 System Identification
  • MAE 567 Vibration and Shock 1
  • MAE 571 Linear System Analysis
  • MAE 576: Mechatronics
  • MAE 577 Computer-Aided Design Applications
  • MAE 578 Cardiovascular Biomechanics
  • MAE 584 Principles and Materials for Micro-Electro-Mechanical Systems (MEMS)
  • MAE 594 Robotics II
  • MAE 670 Nonlinear Control
  • MAE 672 Optimal Control Systems
  • EE 513 Communication Electronics
  • EE 516 Introduction DSP
  • EE 519 Industrial Control Systems
  • EE 520 Quantum Computing and Devices
  • EE 526 Wearable and Implantable sensors
  • EE 531 Probability and Stochastic Processes for Engineering
  • EE 536 Antennas and Propagation for Wireless Communications
  • EE 567 Power Electronics
  • EE 701 Internet of Things

 

Culminating Experience

Students will have the option to complete either a 6-credit master’s thesis or a 3-credit project. The project can be completed either through an internship or through supervision with a faculty member.