PhD Opportunities in AI for Healthcare & Medical Imaging

Johns Hopkins University

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Vacancy

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Description

Applications are being invited for 1–2 doctoral student positions to commence in Fall 2026 within the Department of Electrical & Computer Engineering (ECE) and Data Science & AI (DSAI) at Johns Hopkins University’s Whiting School of Engineering. The positions will be supervised by Assistant Professor Peirong Liu, whose research focuses on the application of artificial intelligence and machine learning in healthcare and medical imaging. The ECE & DSAI doctoral program at Johns Hopkins University is being recognized internationally for its rigorous academic training and cutting-edge research opportunities. Through this program, doctoral students are provided with advanced coursework in electrical engineering, AI/ML, data science, and computational methods, while simultaneously gaining hands-on research experience in interdisciplinary applications. Students will become part of a research environment where innovation is emphasized, collaboration is fostered, and solutions to real-world healthcare challenges are pursued. 

Responsibilities

Opportunities & Responsibilities

Selected doctoral students will be provided with a comprehensive research training experience. Responsibilities will include:

• Participation in ongoing research projects focused on AI applications in healthcare and medical imaging.
• Development and testing of novel machine learning models, image analysis algorithms, and data-driven solutions.
• Contribution to academic publications, conference presentations, and technical reports.
• Collaboration with interdisciplinary teams, including clinicians, engineers, and data scientists.

Hands-on experience with state-of-the-art computational resources, large-scale datasets, and AI/ML frameworks will be offered. Students will also be encouraged to pursue innovative ideas and independent research aligned with the group’s focus areas. The research group led by Professor Liu is being committed to developing AI-driven solutions for medical imaging and healthcare analytics. Projects in the group include the creation of intelligent algorithms for disease detection, predictive modeling, and image-based diagnostics, as well as the translation of AI models into clinical practice. By joining the group, students will contribute to high-impact research that combines engineering expertise, computational modeling, and biomedical knowledge. 

Qualification

Qualifications & Eligibility

Applicants are expected to hold:

1. A Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, Data Science, Biomedical Engineering, or related disciplines.
2. Strong proficiency in programming, machine learning, and quantitative analysis is being sought.
3. Previous experience in medical imaging, healthcare analytics, or AI/ML research is considered advantageous.
4. Candidates must demonstrate a commitment to advancing AI for healthcare applications, strong analytical and problem-solving skills, and the ability to work collaboratively in an interdisciplinary environment. 

Students will become part of a research environment where innovation is emphasized, collaboration is fostered, and solutions to real-world healthcare challenges are pursued. Projects in the group include the creation of intelligent algorithms for disease detection, predictive modeling, and image-based diagnostics, as well as the translation of AI models into clinical practice. By joining the group, students will contribute to high-impact research that combines engineering expertise, computational modeling, and biomedical knowledge. 

Application Process

Interested candidates are being directed to review Professor Liu’s research focus and the detailed application process through the lab’s official website: Peirong Liu Lab – JHU 

Applications are being welcomed from students who aspire to contribute meaningfully to:

1. AI innovations in healthcare and medical imaging
2. And who are motivated to advance research that has both technological and societal impact. 

For more details, please visit here.