PhD in Computational and Data Sciences

Chapman University has cultivated a reputation for fostering a supportive and collaborative learning environment, encouraging students to pursue their academic and professional aspirations with dedication and creativity. With a focus on small class sizes and close faculty-student interactions, Chapman University provides students with the opportunity to engage in meaningful discussions, hands-on experiences, and research opportunities, all of which contribute to a well-rounded educational journey. The university offers a diverse range of undergraduate and graduate programs across various disciplines, including the arts, sciences, business, and health sciences, among others. Chapman's commitment to providing students with a well-rounded education is reflected in its comprehensive curriculum, which combines theoretical knowledge with practical applications, preparing graduates to excel in their chosen fields. Additionally, Chapman University's picturesque campus, vibrant student life, and numerous extracurricular activities contribute to a rich and engaging collegiate experience for students seeking a dynamic and fulfilling educational journey.

Computational Science is the art of creating, developing, and validating models in order to gain a profound understanding of real-life complex problems. Data Science is the art of generating insight, knowledge, and predictions by applying modern methods to large datasets. In Chapman University’s Ph.D. in Computational and Data Sciences program, you will collaborate on innovative research as you work closely with nationally and internationally renowned faculty mentors who will help prepare you to thrive in a variety of professional settings, from academia to private industry, scientific research labs to government agencies. You will learn to design and implement mathematical models and refine quantitative analysis techniques to solve complex scientific problems. Develop your dissertation with focus on advancement of theory and applications of statistical, machine learning and AI in diverse data science related fields such as medicine and epidemiology, climate and Earth hazards, big data and high-performance computing, drug design, genetics, natural language processing, bioinformatics and biotechnology, economics, and sports analytics.