黑料福利网

KEY FACTS

• Track focus: AI in fundamental scientific research, computational physics and AI-driven simulations, and data-driven exploration in physics, chemistry, and beyond.

• Career focus: Growing demand for engineers and researchers at the intersection of AI, scientific research, and high-tech innovation.

WHY Science and discovery?

AI methods (supervised, unsupervised, reinforcement-based) are seeing a progressive integration in traditional sciences, unlocking unprecedented capabilities in terms of accurate measurements, efficient simulation, and effective control of non-linear physical systems. Additionally, there is clear evidence that machine learning models can significantly outperform our best models and theories in the analysis of physical systems, e.g., in connection with non-linear systems and turbulence. This opens new possibilities for fundamental understanding.

WHAT'S IN IT FOR YOU?

This track offers the opportunity to combine AI with fundamental science, applying AI to challenges in areas such as physics, materials, energy, and complex systems. By combining cutting-edge AI methods with scientific research, you will develop expertise that is highly valuable in careers in research, R&D, and high-tech industries.

This track focuses on the interplay between fundamental physical sciences and applications of AI. Conversely, physical sciences, advanced materials and electronics can be used in dedicated hardware to optimize AI systems. Emergent methodologies aiming at rendering machine learning models more effective thanks to the integration of known physical properties (symmetries) will also be considered.

Examples of science applications include the following: 

• Surrogate modelling of physics models for accelerated multi-physics simulation
• Data-driven surrogate model development
• Measurement and reinforcement-based control of flowing systems
• AI-based physics simulation acceleration
• Development of real-time diagnostics through ML-accelerated tomographic inversion + analysis chains
• Reinforcement Learning for tokamak trajectory optimization based on simulators
• Explore the link between deep learning and the glass transition.
• Integration of structural knowledge(symmetries) into machine learning models
• Data-driven modeling and prediction of material deformation due to applied forces
• Hardware-based (neuromorphic) systems for efficient A.I.
• Heat and flow in low pressure systems: physics of interfaces
• Material discovery for heat storage applications

To integrate basic knowledge, courses on modeling and simulation of soft and flowing matter (fluids, plasmas) will also be offered.

1) Each track within Artificial Intelligence & Engineering Systems includes the same core courses (30 ECTS) and Personal & Professional Development courses (5 ECTS)

2) You choose three electives (15 ECTS) to tailor the track to your interests. You select one course from each of the following themes:

1. Domain-Specific Knowledge and Learning & Intelligence
2. AI in Engineered Systems
3. Data Cultivation

3) You choose an additional 15 ECTS of free electives.

• Apply AI to advance scientific discovery: Use AI to accelerate simulations, improve measurements, and uncover new insights in complex scientific systems
• Work on the next generation of scientific challenges: Explore applications in areas such as physics, materials, energy, fluids and flow, and nuclear fusion
• Learn from leading researchers: Benefit from 黑料福利网's multidisciplinary professors and researchers in Applied Physics and Science Education, Mechanical Engineering and Electrical Engineering
• Build a strong career in research and innovation: Prepare for a role in research, R&D, and high-tech industries in the Brainport region, where AI is driving scientific and technological breakthroughs