PhD Position in Mechanical Engineering: AI-Guided Design of Programmable Metamaterials

Position: PhD Position in Mechanical Engineering: AI-Guided Design of Programmable Intelligent Metamaterials
PhD in Mechanical Engineering

Le seguenti informazioni hanno lo scopo di fornire ai potenziali candidati una migliore comprensione dei requisiti per questo ruolo.

Research Title

AI-Guided Design of Programmable Intelligent Metamaterials

Deadline

The 1st of July 2026

Funded by

The Italian Institute of Artificial Intelligence (AI4I), in collaboration with Politecnico di Milano

Supervisors

Marco Maurizi (AI4I)
Valentina Zega (Politecnico di Milano)

Contacts

Websites

R&D Labs - AI4I
_ISSG_AI
- Position Description

The PhD scholarship is funded by the Italian Institute of Artificial Intelligence (AI4I). The research will be carried out jointly at AI4I and Politecnico di Milano. The project focuses on architected materials, also known as metamaterials. The emergence of intelligent and re-programmable metamaterials — engineered systems whose functional properties arise from geometry and architecture rather than chemistry alone — marks a paradigm shift in how materials interact with their environment.

Unlike conventional materials with fixed, intrinsic behavior, these metamaterials can sense external stimuli, process information, and dynamically modify their response in real time. They embody a new class of programmable matter, where structural geometry and embedded functionality combine to achieve adaptive mechanical, acoustic, or electromechanical performance

Such systems hold great promise in fields ranging from vibration and noise control to wearable haptics, soft robotics, and human–machine interfaces. However, the design and control of architectures capable of robust, reversible reconfiguration remain unsolved challenges.

Research Methods and Techniques

The research will combine machine learning, multi-physics simulation, and additive manufacturing in an integrated workflow for designing and realizing re-programmable metamaterials. Key methodologies could include:

Generative deep learning models  (Graph Neural Networks, Diffusion Models, Transformers) to explore design spaces and synthesize architectures with tunable mechanical or electromechanical responses

Physics-informed and multi-objective optimization frameworks,  coupling finite-element simulations with AIbased surrogates to enable rapid evaluation and tuning of programmable states

Embedding actuation and sensing mechanisms  within the geometric design process, supporting heterogeneous architectures that combine passive load-bearing elements with active functional layers or distributed control nodes

Integration of Large Language Models (LLMs)  to assist in translating high-level functional specifications (e.g., “attenuate vibrations in the frequency range 1-10 kHz”) into parametric design constraints

Closed-loop digital workflows  that connect AI design, simulation feedback, and experimental data for model-based re-programming of the fabricated metamaterial

Additive manufacturing and testing  of physical prototypes — such as intelligent vibration absorbers, adaptive haptic surfaces, or acoustically tunable structures — to validate their real-time programmability. Through this synergy between AI-driven design, embedded intelligence, and real-time control, the project will establish the foundations for programmable metamaterials capable of switching functions or adapting to changing environments on demand

Educational Objectives

The doctoral candidate will develop both specialized expertise in intelligent metamaterials and broad scientific and problem-solving skills essential for independent research.

Job Opportunities

The scholarship offers immersion in a multidisciplinary and international research ecosystem, involving collaboration with leading AI scientists and potentially also industrial partners.
Career opportunities could span across research, industry, and technology innovation, where AI and materials design converge. Successful candidates will develop competencies that could be exploited in academic and research positions in computational materials science, mechanics, and AI for engineering design. Potential industrial fields of interest concerning this topic can be aerospace, automotive, and digital manufacturing, among others.
The combination of AI…