Biomedical Engineering Artificial Intelligence M.S.

Program type:

Major

Format:

On Campus

Est. time to complete:

2-3 years

Credit Hours:

Help advance healthcare through artificial intelligence.

Envision yourself in the health care industry? We offer an Artificial Intelligence master's degree with a concentration in biomedical engineering.

Requirements Tuition & Aid How to apply

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Why Earn a degree in Biomedical Engineering Artificial Intelligence?

UNT’s Artificial Intelligence master's degree with a concentration in Machine Learning is interdisciplinary, allowing students to leverage their existing skill set and experience by combining it with AI knowledge. This is a STEM-designated master's program.

Students who graduate from this program will be able to:

Understand and apply the concepts of programming related to AI
Identify and implement AI applications to solve problems
Create and utilize AI systems that respond to market needs
Analyze and apply AI concepts and applications available in their chosen field of interest
Understand the business needs and job market in AI

Marketable Skills

Code using AI programming skills
Design, collect, and analyze data
Solve problems with creative solutions
Quickly grasp new concepts
Collaborate and communicate in teams

Biomedical Engineering Artificial Intelligence Master's Highlights

The College of Engineering has state-of-the-art instructional facilities and laboratories containing cutting-edge research equipment in top-ranked research labs that offer exciting possibilities for study and discovery.

Alongside faculty members, you’ll prepare you for the most in-demand AI jobs by exploring areas of study such as autonomous systems, biomedical engineering, machine learning and natural language processing.

Core courses will dive into deep learning, machine learning, big data and data science, and feature engineering.

Bridge courses provide you the necessary background in programming and include Software Development for Artificial Intelligence and Fundamentals of Artificial Intelligence.

Teaching and research assistantships are available to help you pursue your graduate degree by providing you with a monthly stipend and may qualify you for in-state tuition rates.

This is the only stand alone Master of Science in Artificial Intelligence in the state of Texas and is one of few worldwide.

What Can You Do With A Degree in Biomedical Engineering Artificial Intelligence?

The most in-demand jobs are data scientists, software engineers, and machine learning engineers, but career opportunities in artificial intelligence can span a wide array of disciplines.

Biomedical Engineering Artificial Intelligence Master's Courses You Could Take

Focused on applied signal processing across a variety of modalities including still images and video, audio signals, sensor signals, and natural extensions to other rich multidimensional signals. Students are introduced to computer and simplified biological visual and auditory processing models.

Begins with an introduction to design of experiments as pertaining to biomedical engineering. Examples discussed include sample size, completely randomized design, mean separation procedures, factorial experiments, etc. Also includes exercises in writing IRB and IACUC protocols for in vivo studies in biomedical engineering. Students are required to read, analyze and present critical findings of classic biomedical engineering research papers from Pubmed/Medline.

Contemporary topics in neuroscience and physiology. Topics vary from semester to semester and may include neuro-physiology, computational neuroscience, neurotransmitters, central nervous system trauma.

Design and application of medical instruments. Responsibilities, functions, and duties of the hospital-based biomedical engineer, including program organization, management, medical equipment acquisition and use, preventive maintenance and repair, and hospital safety.

Addresses advances in the science and technology of miniaturization and its applications in biomedical engineering. Advanced techniques to create submicron electromechanical and fluidic architectures, with hands-on lab practice and software modeling. Different types of lithography methods are presented and different techniques such as chemical etching and reactive ion etching are discussed.

A broad understanding of basic techniques for building intelligent computer systems and how AI is applied to solve problems. The emergent nature of intelligent behavior through robust and efficient sensation, knowledge representation, and decision making are demonstrated through a series of hands-on demonstrations and tutorials. Ethical implications of automation and autonomy of machines are discussed through case studies.