Tenure-Track Faculty in Mathematics for AI and Scientific Discovery

IE University´s School of Science and Technology invites applications for a full-time faculty position in Mathematics for AI and Scientific Discovery.

We seek outstanding candidates developing mathematical, statistical, and computational foundations for modern artificial intelligence, particularly researchers interested in using these methods to advance scientific discovery.

This position targets researchers whose primary methodological contribution lies in artificial intelligence, machine learning, probabilistic modeling, or their mathematical foundations, rather than primarily in traditional numerical analysis or computational modeling.

The successful candidate will contribute to undergraduate and graduate teaching in areas such as Mathematics for Artificial Intelligence, Machine Learning, Optimization for AI, Probability and Statistics, Scientific Machine Learning, and AI for Science.

Spanish language skills are not required. Our working language is English. Both our faculty and student body are highly diverse and international.

Research Profile

We are particularly interested in candidates working in one or more of the following areas:

Mathematical foundations of AI: learning theory, approximation theory, high-dimensional probability, information theory, mathematical analysis of neural networks, representation learning, robustness, and interpretability.

Probabilistic modeling and statistical AI: probabilistic machine learning, Bayesian methods, uncertainty quantification, stochastic modeling, and statistical learning.

Optimization for AI: mathematical optimization for machine learning, large-scale and stochastic optimization, variational methods, and optimization of modern AI models.

Scientific machine learning: physics-informed and physics-constrained machine learning, neural differential equations, operator learning, machine learning for dynamical systems, and hybrid mechanistic/data-driven models.

Computational methods for scientific discovery: AI-assisted simulation, surrogate modeling, inverse learning problems, automated scientific discovery, generative approaches for science, and data-driven discovery of mathematical or physical structure.

Research Environment: IE Research Datalab

The successful candidate will have the opportunity to become an active member of the IE Research Datalab, an interdisciplinary research environment within the IE School of Science and Technology bringing together researchers across applied mathematics, statistics, optimization, data science, machine learning, and computational science.

The Datalab combines fundamental methodological research with interdisciplinary and applied research, providing a natural environment for connecting mathematical and statistical methodology with modern artificial intelligence and scientific applications. Its research activities span areas including Data Science and Machine Learning, Applied Mathematics, Operations Research, Statistical Modelling, and Computational Biology.

For this position, particularly relevant areas of interaction include machine learning, Bayesian statistics, probabilistic modeling, optimization, high-dimensional computational methods, and AI-driven scientific applications. The successful candidate will be encouraged to strengthen connections across these areas through an independent research program in mathematically grounded AI and scientific machine learning.

Explore the IE Research Data Lab to know more about its research activities, projects, publications, and faculty.

Impact Xcelerator Collaboration

We particularly value candidates with the capacity to collaborate in applied research projects led by the IEX Labs at IE School of Science and Technology, especially in areas such as AI for Scientific Discovery, Health and MedTech, Advanced Simulation for Energy and Sustainability, Robotics and Autonomous Systems, Digital Twins, and Data-Driven Decision Making.

The ability to build bridges between mathematical foundations, modern AI methodologies, and substantive scientific problems will be central to the role.

Key Responsibilities:

• Develop and teach undergraduate and graduate courses in Mathematics for AI, Machine Learning, Optimization for AI, Probability and Statistics, Scientific Machine Learning, and AI for Science.
• Conduct internationally recognized research in mathematically grounded AI, machine learning, probabilistic modeling, scientific machine learning, or AI for scientific discovery.
• Pursue competitive external research funding.
• Supervise and mentor undergraduate, master’s, and PhD students.
• Build interdisciplinary collaborations connecting mathematics and AI with scientific and technological domains.
• Collaborate with researchers in mathematics, computer science, engineering, and the natural and life sciences, as well as with industry and external research partners.
• Contribute to curriculum development in mathematical AI, scientific machine learning, uncertainty quantification, and AI for Science.
• Participate in academic service and contribute to the strategic growth of the Department of Mathematics and the School of Science and Technology.

We Offer:

• A competitive salary in line with top-ranked European academic institutions.
• A generous seed research fund to cover travel, equipment and relocation costs.
• State-of-the-art campus facilities and support to build your lab infrastructure.
• A professional and stimulating international working environment with English as the working language.
• Full access to public health services through membership of the National Insurance Scheme, plus a discount plan to acquire an additional private health insurance plan.
• An open, inclusive, and family-friendly academic environment.
• Inspiring campuses in the heart of Madrid and Segovia.

About IE University and the School of Science and Technology

IE University is an internationally recognized institution originally founded as a business school. We consider ourselves the most international institution of higher education with approximately 85% international students representing more than 120 countries.

The School of Science and Technology offers undergraduate and graduate programs in Applied Mathematics, Data Science, Artificial Intelligence, Computer Science, and related STEM disciplines, fostering excellence in research, innovation, and interdisciplinary education.

The School is committed to advancing research at the intersection of mathematics, statistics, computation, and artificial intelligence, with particular emphasis on emerging approaches that combine mathematical foundations and AI to accelerate scientific discovery.

Qualifications

• PhD in Applied Mathematics, Statistics, Computer Science, Computational Science, Physics, Engineering or relevant field by the appointment date, or a closely related area.
• Postdoctoral research experience is desirable but not required. Evidence of excellent research potential or achievement, including recent high-quality publications.
• Potential to attract competitive external funding.
• Commitment to excellent teaching, student supervision, and inclusive mentorship.
• Ability to contribute to interdisciplinary science and technology programs.

Particular interest will be given to researchers developing methods with applications in areas such as physics, engineering, climate and environmental science, energy, materials science, life sciences, neuroscience, health, or other scientific domains.

Faculty are expected to engage in high-quality research, pursue competitive external funding, supervise students, and contribute to curriculum development and university service.

Application Instructions

Create your dossier at Interfolio by October 31st and apply at: https://apply.interfolio.com/192572

Applications received before November 1st, 2026 will receive full consideration. The position will remain open until it is filled.

Application Materials

• Cover letter.
• Curriculum vitae, including recent publications, funding record, teaching experience and supervision record.
• Research statement, including main achievements and future research plans for the next five years.
• Teaching statement.
• Evidence of teaching quality, if available.
• Up to three representative publications.

Shortlisted candidates invited to an online interview may be asked to provide two to three confidential recommendation letters at a later stage.

Pre-submission inquiries about the open positions may be addressed to Prof. Dae-Jin Lee Dae-Jin.Lee@ie.edu

For questions about your dossier, please go to: Interfolio Dossier Help

Equal Employment Opportunity Statement

IE University is strongly committed to non-discriminatory hiring practices, and encourages applications from individuals who will further expand the diversity of our faculty.