Interdisciplinary PhD training in Algorithms, Combinatorics, and Optimization at Carnegie Mellon University, spanning computer science, mathematics, and operations research. Students develop research depth in rigorous theory and scalable computational methods.
The PhD program in Algorithms, Combinatorics, and Optimization at Carnegie Mellon University offers an interdisciplinary training experience that encompasses computer science, mathematics, and operations research. This program is designed to equip students with both theoretical and practical skills, fostering research depth in rigorous theories and scalable computational methods.
The program is jointly administered by the Tepper School of Business (Operations Research), the School of Computer Science (Algorithms), and the Department of Mathematics (Discrete Mathematics). Students will engage in core research-oriented coursework that lays the foundation for advanced study and research.
Note: The specific course plan, number of courses, and any required milestones, such as qualifying exams, will be determined by the student's advisory committee in accordance with departmental policies.
While specific GRE/GMAT scores or minimum GPA thresholds are not stipulated, applicants should possess a strong foundational knowledge in relevant quantitative fields, including mathematics, computer science, operations research, or related disciplines. Additionally, it is advisable to review the university's current English proficiency policy for graduate admissions.
Graduates of this program are well-prepared for careers in academia, industry, and research. With expertise in algorithms, combinatorics, and optimization, they can pursue roles in fields such as data science, operations research, software development, and quantitative analysis. The skills acquired during the program also open doors to leadership positions in various sectors, including technology, finance, and logistics.
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