M.S. in Neural Technologies (MiNT)

This interdisciplinary initiative, jointly offered by the Neuroscience Institute and the Department of Biomedical Engineering, prepares you for a career at the nexus of neuroscience and engineering.

Degrees Offered

Application Deadline

February 15 (MiNT-R/MiNT-AS)

April 30 (MiNT-A only)

Graduate Application Process

Choose Your Path

To accommodate a wide range of academic backgrounds and career goals, the MiNT program offers three distinct master's degrees. Explore the options below to find the program that is the best fit for you:

The MiNT-R Program

Your Path to a Research Career

The Master’s in Neural Technologies - Research (MiNT-R) program is designed for students who aspire to pursue a Ph.D. or a research-intensive career in academia or industry. This degree will equip you with the advanced knowledge and independent research skills needed to advance our understanding of brain function and intelligent systems.

  • Duration: A full-time, five-semester (72-week), 147-unit degree.
  • Target Audience: Ideal for students with a bachelor's degree in a relevant area such as physics, biology, chemistry, mathematics, computer science, neuroscience or engineering.
  • Prerequisites: Successful applicants are expected to have a strong background in basic science or engineering, statistics and data science, and a basic proficiency in at least one programming language (e.g., Python, R, C++).

The program's structure ensures a balanced foundation in both neuroscience and your chosen specialization track. You will be required to complete courses in the following areas:

  • 3 neuroscience core courses
  • 1 track core course in either the NIRE or NCAI track
  • 3 track elective courses

A major component of the MiNT-R program is your independent research. You will be matched with a faculty lab to conduct cutting-edge research, culminating in a formal master's thesis. This includes:

  • A formal research proposal
  • An approved research thesis based on your findings

The program is structured to allow you to build your knowledge base before diving into full-time research. A typical timeline looks like this:

  • Year 1: Focus on completing your required coursework to build a strong theoretical foundation.
  • Summer: Engage in continuous research in your assigned lab to advance your thesis project.
  • Year 2: The primary focus shifts to your thesis project, including writing your proposal and completing your research for the final thesis.

The MiNT-AS Program

Innovate in Industry

The Master’s in Neural Technologies - Applied Study (MiNT-AS) program is tailored for students who are interested in the practical application of neurotechnology. This degree prepares you to take a leadership role in developing innovative technologies within industrial or applied research settings.

  • Duration: A full-time, five-semester (72-week), 147-unit degree.
  • Target Audience: This program is best suited for students with a bachelor's degree in a foundational discipline like physics, biology, chemistry, mathematics, neuroscience or engineering.
  • Prerequisites: Applicants should have a strong background in basic science or engineering, statistics and data science, and a basic proficiency in at least one programming language (e.g., Python, R, C++).

The curriculum is designed to give you a strong academic foundation before moving into professional application. You will be required to complete courses in the following areas:

  • 2 Neuroscience Core courses
  • 1 Track Core course in either the NIRE or NCAI track
  • 2 Track Elective courses

This program emphasizes hands-on experience and professional skills. After your first year of coursework, you will complete a summer internship with a neurotechnology company or a CMU lab.

Your second year is dedicated to a two-semester capstone project. You will work in teams on a real-world project, taking a product from its initial concept and design through prototyping, testing, and consideration of regulatory and intellectual property issues. This capstone is where you'll use your skills to build a functional, usable technology, with an emphasis on teamwork, clear communication and delivery.

The MiNT-A Program

For CMU's Top Undergraduates

The Master’s in Neural Technologies - Accelerated (MiNT-A) is a program exclusively for high-achieving Carnegie Mellon University undergraduate students who are completing a Bachelor of Science in Neuroscience. It offers a streamlined pathway to advance your academic and research pursuits immediately after graduation.

Important Note: To be eligible for this program, courses used toward your undergraduate major cannot be double-counted toward the master’s degree.

  • Duration: A two-semester, 90-unit accelerated degree.
  • Target Audience: Students currently completing a B.S. in Neuroscience at CMU who are interested in pursuing research in neural technologies in an academic or similar setting.
  • Prerequisites: Admission is selective and based on academic performance. Applicants must have a strong background in statistics and data science and basic proficiency in a programming language (e.g., Python, R, C++), with a preference for students who have some training in engineering.

This program builds directly on your undergraduate foundation, providing an intensive, research-focused experience. You will be required to complete courses in the following areas:

  • 1 Track Core course in either the NIRE or NCAI track
  • 4 Track Elective courses
  • Research credits that contribute to your thesis

A significant part of the MiNT-A program is your independent research project. You will work with a faculty mentor to produce an original thesis that showcases your research skills and expertise. The program culminates with writing a comprehensive master’s thesis.

MiNT Core Courses and Electives

COURSE

COURSE TITLE

03-762 

Advanced Cellular Neuroscience

03-763 

Systems Neuroscience

86-765

Foundations of the Neural Basis of Cognition

COURSE

COURSE TITLE

42-630

Introduction to Neural Engineering (NIRE) 

86-718

Brain Computation (NCAI) 

COURSE

COURSE TITLE

03-766

Advanced Neuropharmacology: Drugs, Brain and Behavior

16-711

Kinematics, Dynamics Systems, and Control

16-722*

Sensing and Sensors

16-741*

Mechanics of Manipulation

16-761*

Mobile Robots

16-811*

Mathematical Foundations of Robots

16-831*

Introduction to Robot Learning

18-578

Mechatronic Design

18-675

Autonomous Control Systems

18-743

Neuromorphic Computer Architecture & Processor Design

24-673

Soft Robots: Mechanics, Design and Modeling

24-678

Special Topics: Computer Vision for Engineers

24-755

Bioinspired Robot Design and Experimentation

42-633

Brain-Computer Interface: Principles & Applications

42-640

Image-based Computational Modeling & Analysis

42-651

Fundamentals of MRI and Neuroimaging analysis

42-652

Nano-bio-photonics

42-663

Engineering Principles of Medical Devices

42-696

Special Topics: Wearable Health Technologies

42-699

Neural Technology: Sensing and Stimulation

42-733

ST: Data-Driven AI for Dynamic Systems Control with Application to Neural Data

42-737

Biomedical Optical Imaging

85-735

Biologically Intelligent Exploration

*Program Approval Required

COURSE

COURSE TITLE

02-680*

Essential Mathematics and Statistics for Scientists

02-712*

Computational Methods for Biological Modeling and Simulation

10-701*

Machine Learning

10-715*

Advanced Machine Learning

10-733*

Representation and Generation in Neuroscience and AI

10-747*

Neuro-symbolic AI

15-686*

Neural Computation

15-780*

Artificial Intelligence

15-833*

Computational Models of Neural Systems

16-720*

Computer Vision

16-722*

Sensing and Sensors

16-822*

Geometry Based Methods in Vision

16-823*

Physics-based Methods in Vision

16-831*

Introduction to Robot Learning

18-675

Autonomous Control Systems

24-677*

Modern Control Theory

24-678

Special Topics: Computer Vision for Engineers

36-705*

Intermediate Statistics

42-631

Neural Data Analysis

42-632

Neural Signal Processing

42-656

Introduction to Machine Learning for Biomedical Engineers

42-678

Medical Device Innovation and Realization

42-698

Special Topics: Clinical Translation of AI

42-699

Neural Technology: Sensing and Stimulation

42-733

ST: Data-Driven AI for Dynamic Systems Control with Application to Neural Data

42-737

Biomedical Optical Imaging

85-712

Cognitive Modeling

85-719

Introduction to Parallel Distributed Processing

85-732

Data Science for Psychology & Neuroscience

85-735

Biologically Intelligent Exploration

85-767

Cognition in the Age of AI

85-813

Perception

86-752*

Principles of NeuroAI

*Program Approval Required