Advancing Scientific Discovery and Innovation
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Contact us to learn more about collaboration and sponsorship opportunities.
Research Context & Public Mission →Join us in advancing scientific discovery and innovation.
Four research projects are currently available for external funding.
Funding is available for PhD students, Junior, and Senior researchers.
Target Audience & Funding Structure →
We develop a novel mathematical framework for elasto-plastic deformation in solids, combining rigorous theory with numerical simulation.
The model aims to describe how materials transition from reversible elastic behavior to irreversible plastic flow driven by microstructural mechanisms such as dislocation dynamics.
Applications include: failure prediction in deformable solids, advanced manufacturing processes (forming, forging, extrusion), and the simulation of next-generation engineered materials.
We develop advanced mathematical models and discrete optimization methods for the design and management of forest infrastructures.
The focus is on improving accessibility, resilience, and operational efficiency of forest road networks under uncertainty, particularly in emergency situations such as wildfires.
Application domain: optimal design of forest road systems to support wildfire prevention and response, ensuring rapid and safe access for firefighting operations while minimizing environmental and construction costs.
We develop mathematical and computational methods based on geometry and topology for the analysis of complex high-dimensional and time-dependent data.
The approach focuses on extracting robust structural information from data using Topological Data Analysis (TDA), enabling multi-scale representation and intrinsic shape-based inference.
Applications include: machine learning and AI, robotics, anomaly detection, neuroscience and cognitive science, audio and signal processing, biomedical data analysis, environmental systems, and financial time series modelling.
We investigate methods from proof theory and mathematical logic to extract quantitative information from non-constructive proofs.
The goal is to transform qualitative existence results into explicit computational content, including rates of convergence, effective bounds, and algorithmic structures.
Applications include: reliable computation, verified optimization, provable machine learning, safety-critical control systems, explainable AI, and complexity and resource analysis.
A novel model of Elasto-plasticity (theory & numerical simulations). This project focuses on developing advanced models to predict failure and defects in deformable solids. Applications include manufacturing and forming processes, as well as numerical simulations of new materials.
Key Objectives:
Potential Impact: Improved material design, manufacturing efficiency, and defect prediction.
Contact Prof. Nicolas Van Goethem directly about this project.
Prof. Van Goethem's personal webpage
gmferreira@ciencias.ulisboa.pt View Full Project Description →Mathematics for Forest Protection and Sustainability: This project aims to optimize access for firefighters to control forest fires effectively.
Key Objectives:
Potential Impact: Enhanced forest protection, reduced fire damage, and sustainable forest management.
Potential Impact: Advanced data analysis techniques for diverse scientific and industrial applications.Contact Prof. Agostino Agra directly about this project.
View Full Project Description →Analysis of time series data via topological data analysis. This project explores the application of topological data analysis to various fields.
Topological Data Analysis (TDA) is a modern mathematical framework that reveals meaningful patterns in complex data by studying its shape across multiple scales, enabling deeper insight into evolving systems that traditional single-scale methods often miss.Key Applications:
Potential Impact: Advanced data analysis techniques for diverse scientific and industrial applications.
Contact Prof. Florian Pausinger directly about this project.
Prof. Pausinger's personal webpage
View Full Project Description →Extracting Quantitative Knowledge from Proofs: This project focuses on developing logical tools for reliable computation and optimization.
Key Applications:
Potential Impact: Enhanced reliability and safety in computational systems and AI applications.
Contact Prof. Gilda Ferreira directly about this project.
Prof. Ferreira's personal webpage
View Full Project Description →For more information about the projects, Download the detailed PDF or contact us directly.
Private entity managing the funds: FCiências.Id
Any questions about fund management, scientific funding, tax benefits, and corporate sponsorship.Research center: CEMS.UL