By its nature, the doctorate is multidisciplinary, covering three areas of knowledge that have been selected among the research lines with greater strength within the School of Engineering and Sciences. These areas interact with each other through projects, centers and focus groups.


The Knowledge Generation and Application Lines of the program are:

  1. Bio-Inspired Systems (Bio-inspired Algorithms)
  2. Machine learning models (Machine Learning)
  3. Data Science and Applied Mathematics (Data

Entry requirements

  • Previous studies. A master's degree with a minimum average of 90 or its equivalent.
  • Evidence of English language proficiency Applicants to the doctoral program must submit evidence of having taken the TOEFL or other equivalent exam. The admitted student must have a minimum of 550 points or its equivalence in another TOEFL format or its equivalence in the other exam.
  • Admission Test. Obtain a minimum score of 600 points in the Graduate Studies Admission Test (PAEP).
  • Letters of recommendation: Present at least three letters of recommendation from Academics (professors, thesis advisor, etc.) who know thoroughly the academic performance and attitudinal qualities of the applicant.
  • Test of motives. An essay in which the student justifies his reasons and goals to pursue doctoral studies, specifying the area of ​​specialization chosen, as well as a description of his area of ​​knowledge.
  • Interview: Those interested in the doctoral program must conduct an interview with the person or persons decided by the Admissions Committee of the Program. In the interview, the applicant's motivation for carrying out postgraduate studies and the knowledge of the work developed in the different lines of research of the Postgraduate Program will be evaluated.

Once the student has presented the aforementioned documents and tests, his / her file will be evaluated by the Admissions Committee of the DCC, which will be composed of the program director and at least one representative of each LGAC of the DCC, which initially has 3 LGAC, so that the Admissions Committee will have at least four members, the academic faculty being able to request that other professors from the same faculty join. The decision of the Admissions Committee will be unappealable.

Call for Scholarships

The Doctorate in Computer Science is aimed at professionals with a Master's degree in computer science, engineering and exact sciences mainly interested in high impact research, to contribute to the knowledge of some of the areas of specialty of Computer Science.

The graduate program is of national coverage, currently taught at the Monterrey and State of Mexico campuses.

Knowledge generation and application lines

The Master in Computer Science is aimed at professionals in the areas of computer science, engineering and exact sciences mainly interested in conducting high impact research, to contribute to the knowledge of some of the areas of specialty of Computer Science.

  1. Bio-Inspired Systems (Bio-inspired Algorithms). This line of research focuses on the development, extension and modification of algorithms and methods to solve complex problems systematizing informal solutions in heuristic and mathematical models.
  2. Machine learning models. This line focuses on the investigation of computational models of learning with the aim of forecasting or identifying behaviors on a set of data or examples of input and that leads to better decision making.
  3. Data Science and Applied Mathematics (Data

Financial Support

The Tecnológico de Monterrey offers a total tuition scholarship. To be a candidate for the scholarship, you must be a full-time student and comply with the program's admission requirements.

* Ask about the child support options.

Advantage

Teachers

Program taught by professors with doctorate degree and professional experience of the School of Engineering and Sciences of Tecnológico de Monterrey.

Accreditations

This program has accreditations and recognitions from national and international institutions such as:

  • Program accredited by the National Graduate Register of Quality (PNPC) of CONACYT.
  • Commission of Universities of the Association of Schools and Universities of the South of the United States (SACS).
  • The Tecnológico de Monterrey is accredited by the Commission of Universities of the Association of Schools and Universities of the South of the United States to grant professional degrees and academic degrees of masters and doctorates. Contact the College Commission at 1866 Southern Lane, Decatur, Georgia 30033-4097, or call (1) 404-679-4500 (1) 404-679-4500, for questions about the Tecnológico de Monterrey accreditation.
  • National Postgraduate Program in Quality (PNPC) of the National Council of Science and Technology (CONACYT).
  • Recognition of official validity of the Secretariat of Public Education of Mexico.

Educational model

The active participation of students in their professional and personal education is promoted through individual and collaborative learning. This model also allows the student to build their knowledge with the guidance of expert teachers in their professional field and in teaching.

goals

  • To train independent researchers, with skills, knowledge and skills to identify opportunities, develop, and direct original research projects at the frontier of knowledge.
  • Disseminate the results of such investigations, and apply the knowledge generated in the technological development of the country. To be recognized as a high-impact computer science program in the productive, educational-academic and social sectors of the country.

Lines of investigation

  1. Bio-inspired Systems (Bio-inspired Algorithms) This line of research focuses on the development, extension and modification of algorithms and methods to solve complex problems by systematizing informal solutions in heuristic and mathematical models. Many real problems when they grow in size are difficult to model using mathematical tools, but nature shows us through many examples how it is possible to synthesize the complexity to a function that can be solved in a practical way. However, the computational emulation of the particular problem to solve is not simple and requires in-depth investigation of many aspects. The techniques investigated are based on computational intelligence that includes evolutionary computation, neural networks and fuzzy logic in the first instance. Other techniques inspired by nature also considered are artificial immune systems, swarm intelligence and simulated annealing. Within the research it is important to study families of problems related to optimization, design, verification and forecasting that impact areas of application such as logistics, manufacturing, industrial processes, bioinformatics, genomics and computer finance.

  2. Machine learning models This line focuses on the investigation of computational models of learning with the aim of forecasting or identifying behaviors on a set of data or input examples that leads to better decision making. The study and development of different learning algorithms is the primary objective of this group and in which a variety of supervised or unsupervised learning methods stand out, class or multi-class classification algorithms, grouping algorithms, reinforcement learning, recognition of patterns, among others. We also investigate how to complement the behavior of the previously mentioned techniques through others such as artificial symbolic intelligence, multi-agent systems, semantics and ontologies, and knowledge of the context. Everything researched in this line has a wide range of applications, such as health, energy, security (computing), social networks, environmental intelligence, ubiquitous computing, and is relevant in multi-disciplinary disciplines such as big-data, data analytics and business intelligence.

  3. Data Science and Applied Mathematics (Data
Program taught in:
  • Spanish

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Jan 2020
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