The Pre-Doctoral Training Program in Computational Medicine, funded by the National Institute of General Medical Sciences under Award Number T32GM119998, supports selected trainees from the departments of Biomedical Engineering and Applied Mathematics & Statistics.

Students chosen for this Ruth L. Kirschstein National Research Service Award institutional training grant will learn through a combination of focused coursework and dissertation research alongside computational medicine training program faculty mentors from across the Johns Hopkins Whiting School of Engineering and the School of Medicine. The program is designed to prepare graduates to fill the growing need for researchers trained in computational medicine in both industry and academia.

Trainees will be part of an exceptional and distinctive community of students and Training Program Faculty exploring the possibilities of computational medicine. Trainees will learn how to develop models of biological systems in health and disease, constrain these models using data collected from patients, and apply models to deliver improved diagnoses and therapies.

Prospective trainees should apply to the PhD programs of the Departments of Biomedical Engineering or Applied Mathematics and Statistics, indicating an interest in pursuing predoctoral training in Computational Medicine.

Program Milestones

In addition to the requirements of your home department’s PhD program, you have the following commitments as a member of the training program in computational medicine:

1. 18 credit hours of computational medicine electives prior to degree completion.

   • Sample Elective Courses

     Course Number	Course Title	Credits
     EN.540.621	Project in Design: Pharmacodynamics	3
     EN.540.632	Project in Design: Pharmacokinetics	3
     EN.553.650	Computational Molecular Medicine	4
     EN.580.631	Introduction to Computational Medicine: Imaging	2
     EN.580.633	Introduction to Computational Medicine: The Physiome	2
     EN.580.639	Models of the Neuron	4
     EN.580.640	Systems Pharmacology and Personalized Medicine	4
     EN.580.647	Computational Stem Cell Biology	3
     EN.580.664	Introduction to Data Science for Biomedical Engineering	4
     EN.580.680	Precision Care Medicine I	4
     EN.580.681	Precision Care Medicine II	4
     EN.580.688	Foundations of Computational Biology and Bioinformatics	4
     EN.580.691	Learning, Estimation, and Control	3
     EN.580.697	Neuro Data Design	3
     EN.580.743	Advanced Topics in Genomic Data Analysis	3
     EN.580.745	Mathematics of Deep Learning	1.5
     EN.520.621	Introduction to Nonlinear Systems	3
     EN.520.622	Principles of Complex Networked Systems	3
     EN.520.632	Medical Imaging Systems	3
     EN.540.639	Advanced Topics in Pharmacokinetics and Pharmacodynamics	3
     EN.601.647	Computational Genomics: Sequences	3
     EN.601.648	Computational Genomics: Data Analysis	3
     EN.601.675	Machine Learning	3
     EN.601.676	Machine Learning: Data to Models	3
     EN.601.682	Machine Learning: Deep Learning	3
     EN.601.749	Computational Genomics: Applied Comparative Genomics	3

2. Responsible Conduct of Research (AS.360.625) or SOM equivalent (must be in-person)
3. At least one rotation in a lab of Training Program Faculty.
4. A significant collaboration with a clinician-researcher who works with patient data.
5. Advanced Seminars in Computational Medicine (EN.580.735) – includes attending ICM Distinguished Seminar Series and Computational Medicine Journal Club*
6. Attendance at annual ICM Retreat*

* As you are considered a participant in this program for the entirety of your PhD training, these obligations and opportunities continue beyond the end of your one-year appointment.