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.
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:
|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.601.676||Machine Learning: Data to Models||3|
|EN.601.682||Machine Learning: Deep Learning||3|
|EN.601.749||Computational Genomics: Applied Comparative Genomics||3|
* 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.