Investigador Postdoc IFE Ciencia de Datos y Machine Learning

Postdoctoral Position for  Institute for the Future of Education.

The Institute invites ambitious and early-career researchers to apply for postdoctoral positions to develop interdisciplinary research on educational theories that enhance the teaching-learning process at a global level. This position is a one-year contract (renewable for an additional year in case of outstanding performance). Part of the responsibilities may include working with direct supervisors as a teaching assistant, not exceeding 25% of the total time. 

Responsabilities:

• Design research on the teaching-learning process.
• Apply statistical methods for data analysis in educational innovation.
• Develop strategies for submitting grant applications for research.
• Write reports in English for submission to SCOPUS Q1/Q2 journals.
• Participate in academic activities of the IFE.  

Expected outcomes:

• Apply for research grants in scientific fields.
• Independently write scientific research articles.
• Develop strategies to improve the teaching-learning process.
• Collaborate with other IFE researchers.
• Participate in Education Conferences

Qualifications:

• Demonstrated potential for exceptional leadership and a strong commitment to developing an exciting and innovative research agenda that leads to high scientific impact, technological development, or opportunities for project coordination and resource management. Previous experience in educational research in education, computing, science, engineering, or artificial intelligence is an advantage. Additionally, skills such as writing grant proposals and manuscripts for publication are preferred.

• Doctoral degree in the following areas of knowledge: Science, Engineering, Computing, or related fields.
• Published articles in scientific journals indexed in Scopus.

Professional Experience

• Prior experience in the discipline is desirable (e.g., participation in international research or development projects, research stays, or previous postdoctoral fellowships).
• Open to interdisciplinary collaboration.
• Proven oral and written English proficiency at least at B2 level according to the Common European Framework of Reference for Languages (CEFR).
• A Ph.D. in one of the following fields: Computer Science, Artificial Intelligence, Applied Mathematics, Education, Cognitive Science, or Psychology (preferably awarded in 2017 or later).
• Experience in research projects and/or academic activities. Preference will be given to candidates with experience in educational projects.
• At least four published articles in a scientific journal indexed in Scopus.

The salary is competitive and commensurate with experience and qualifications. A comprehensive benefits package is provided as well.

Location: Monterrey, México.

About Tecnologico de Monterrey

Tecnológico de Monterrey is a private non-for-profit institution with over 24 campuses in Mexico. It has more than 89,000 students and 230,000 alumni. Our mission is to educate proactive, humanistic, and internationally-competitive leaders for the academic, business,civic, and government sectors. With over 100 academic programs at the undergraduate and graduate level, we have established a reputation for providing educational experiences of the highest quality and fit for an increasingly globalized and digitalized world.

Our university is 161st in the world, one of the Top 5 in Latin America and Top 30 of the private universities in the world, according to the QS World University Ranking. The Times Higher Education World University Ranking considers it #1 in Mexico and #5 in Latin America. In addition, our university has been accredited since 1953 by the Southern Association of Colleges and Schools of the United States. For more information about Tecnologico de Monterrey, please visit: https://www.tec.mx/en

Application process

Email cover letter, CV, and two letters of reference to karen.ortiz@tec.mx

At Tec de Monterrey we act under the principle of equal opportunities. We do not discriminate based on age, ethnic origin, nationality, gender, sexual orientation, marital status, social status, health status, religious beliefs, political doctrine, or disability.