DESCRIPTION

The aim of the BIP is to provide students with the basics of mathematical and computational modeling techniques with applicability in developing digital models and supporting sustainable decision making in different domains such as health, environmental, societal and industrial domains. The program is designed for students at Master or PhD level and young researchers who wish to develop these skills to apply them in research projects aligned to the EU GREEN research themes. This programme prioritizes high-level strategic thinking in the design and analysis of modelling paradigms and how they can be applied for effective decision-making. Interpersonal skills, together with technical skills, are essential for learning outcomes.

OBJECTIVES OF THE STUDY PROGRAMME (BIP)
- Introduction to Mathematical and Computational Modelling for Sustainability
- Familiarization with fundamental approaches to mathematical and computational modeling methods, techniques and tools, including differential equations, machine learning/AI, finite difference/element/volumetric methods, cellular automata and optimization algorithms.
- Develop awareness of methods that may be suitable for different types of research problems, together with the potential benefits, challenges and limitations of each approach
- Develop skills in application of these methods to real-world multidisciplinary research problems aligned to the EU Green themes
- Develop student transferable skills in group working and communication
- Help to create a culture of international and cross-discipline collaboration in postgraduate research at the EU Green institutions

Postgraduate students will develop skills in translating broad-ranging real-world problems in a mathematical/computational framework and be familiar with fundamental tools in analysing models for problem solving, particularly in the context of sustainability. The programme will focus on familiarity with different methods, their potential applications and their limitations and will enable students to select specific topics for further in-depth study.