Overview

Research & Collaboration

The AI and Scientific Computing Research (AISCR) fosters a collaborative research ecosystem that brings together universities, industries, and research community to advance the frontiers of AI-driven discovery and computational science. Our mission is to promote interdisciplinary innovation and ensure that artificial intelligence and computational methods lead to meaningful scientific, technological, and societal progress.

Collaboration Domains

Engineering & Technology

Application of AI in design, control, and simulation

Projects

Intelligent Control Systems, Predictive Maintenance, Smart Grids

Health & Life Sciences

Computational modeling and AI in medicine

Projects

Drug Discovery, Medical Imaging, Bioinformatics

Fundamental & Environmental

Sciences AI for modeling and understanding complex systems

Projects

Quantum Simulations, Climate Modeling, Earth Observation

AI Algorithms & Scientific

Computing New methods for data-driven discovery

Projects

Machine Learning Optimization, Neural Simulators, High-Performance Computing

Collaboration Opportunities

AISCR invites partnerships across sectors:
•  Academic Institutions — joint research, exchange programs, and co-supervision.
•  Researchers & Scientists — interdisciplinary and data-driven projects.
•  Industry & Startups — AI-based innovation and applied computational research.
•  NGOs — policy-driven AI research for sustainability and development.

How to Collaborate

Mode
Description
Joint Research Projects
Co-develop AI or computational research with AISCR experts.
Data & Model Sharing
Collaborate on open datasets, models, or simulations.
Research Internships / Fellowships
Participate in supervised research projects.
Proposal Partnerships
Partner on EU / Horizon / NSF grant proposals.

Publications & Outputs

AISCR regularly publishes research outcomes in top-tier journals and conferences.
Key focus areas include:
• AI for Scientific Discovery
• Computational Modeling and Simulation
• Data-Driven Materials & Systems
• Responsible and Reproducible AI