R&D/AMED TWO POSTDOCTORAL RESEARCHER POSITIONS

FONDAZIONE AI4I-CENTRO ITALIANO RICERCA AUTOMOTIVE; Italia; In loco; EUR 30.000 - 55.000; Tempo pieno

Descrizione del lavoro

A research institution in Italy is seeking postdoctoral researchers for innovative projects in generative AI for architected materials. The roles involve designing AI models and collaborating with renowned labs. Ideal candidates have a PhD in AI or engineering and a strong publication record. The positions offer a competitive salary and opportunities for professional growth within a stimulating research environment.

Servizi

Competitive salary

Access to high-performance computing

Collaborative research environment

Competenze

• PhD in applied AI, mechanical/civil/materials engineering, or related field.
• Strong publication record in impactful journals or conferences.
• Proficient in a modern deep learning framework.

Mansioni

• Design and train novel generative models for metamaterials.
• Lead prototyping and validation in collaboration with partner labs.
• Implement ML-based design strategies for adaptive structures.

Conoscenze

Cutting-edge generative AI

Physics-informed ML

3D metamaterials design

Communication skills

Formazione

PhD in applied AI or engineering

Strumenti

PyTorch

Abaqus

Descrizione del lavoro

Overview

Postdoctoral Research Positions in Generative AI for Architected Materials

Lab: AI for Advanced Materials & Engineering Design (AMED) at AI4I – Italian Institute of Artificial Intelligence for Industry, Turin. PI: Dr. Marco Maurizi. Two postdoctoral researcher positions to drive foundational research at the intersection of artificial intelligence, physics-based modelling, and metamaterials design. Each position is tied to one of the Lab’s two flagship research areas.

Position 1: Physics-Informed Generative AI for Architected Materials

This project explores the use of cutting-edge generative AI — including graph neural networks, diffusion models, and transformer architectures — to automatically generate and optimize micro-architected materials (e.g., truss lattices, shell-based structures, voxelized media). The goal is to incorporate physics priors and manufacturing constraints directly into the generative process, enabling efficient inverse design of manufacturable materials with complex functionalities (e.g., vibration damping, impact protection, programmable fracture).

The project\'s scope is intentionally broad. Candidates are encouraged to propose specific research directions that align with the overall goals, which will be discussed and refined in collaboration with the PI, Dr. Marco Maurizi.

Key Responsibilities

• Design and train novel generative models for 3D metamaterials.
• Investigate embedding mechanical and physical constraints into learning-based design workflows.
• Collaborate on simulation pipelines (finite element and reduced order models).
• Lead prototyping and experimental validation in collaboration with partner labs (UC Berkeley, Polytechnic of Milan).

Position 2: Machine Learning-Guided Design of Programmable Intelligent Metamaterials

This project targets the creation of intelligent metamaterials — adaptive systems with integrated sensing, actuation, and control — through AI-guided design frameworks. The candidate will explore multimaterial, modular, and reconfigurable systems generated via deep learning models and potentially enhanced through large language models to interpret high-level design intent or physical behaviour.

The project\'s scope is intentionally broad. Candidates are encouraged to propose specific research directions that align with the overall goals, which will be discussed and refined in collaboration with the PI, Dr. Marco Maurizi.

Key Responsibilities

• Develop ML-based generative design strategies for active structures (e.g., sensing, actuation).
• Implement finite element or reduced order models to generate datasets aligned with the proposed generative design pipeline.
• Explore hybrid workflows combining machine learning, symbolic priors, and LLMs.
• Support fabrication and experimental validation of responsive prototypes (e.g., acoustic metamaterials, haptic devices) in collaboration with UC Berkeley and Polytechnic of Milan partner labs.

What We’re Looking For

We are looking for talented and highly motivated postdoctoral researchers with a passion for pushing the boundaries of science and engineering through machine learning and computational design. The ideal candidates are intellectually curious, unafraid to challenge conventions, and driven to solve ambitious problems at the intersection of AI, solid mechanics, and materials science. We value creativity, scientific rigour, and a collaborative spirit.

Preferred Qualifications

• A PhD in applied AI, mechanical/civil/materials engineering, or a related field.
• A strong publication record in high-impact journals or top machine learning conferences (e.g., NeurIPS, ICLR, ICML, CVPR).
• Proficiency in at least one modern deep learning framework (e.g. PyTorch, Tensorflow, JAX).
• Experience with finite element modelling (e.g., Abaqus, COMSOL Multiphysics) or reduced order models.
• A demonstrated ability to work across disciplines, with excellent communication skills.
• Prior work on architected materials (including composites), design automation or physics-informed ML is an advantage.
• Hands-on experience with large language models (LLMs) or integrating LLMs in scientific workflows is an advantage.
• A solid understanding of solid mechanics is an advantage.

What We Offer

• A stimulating, ambitious and collaborative research environment within AI4I’s international, interdisciplinary ecosystem.
• The opportunity to co-author high-impact publications and help define emerging paradigms in AI-guided materials design.
• Tailored mentoring to support long-term academic or industry career trajectory.
• Opportunities to grow leadership and mentoring skills, including involvement in proposal writing and supervision of PhD students.
• Access to high-performance computing (HPC) infrastructure and state-of-the-art fabrication and testing facilities through established collaborations (UC Berkeley and Polytechnic of Milan).
• Competitive salary and travel support. Both positions are initial one-year appointments with the possibility of annual renewal based on performance and continued funding.

Salary range: €30,000 – €55,000 gross per year, depending on experience. Researchers relocating from abroad may be eligible for tax exemptions of up to 90%.

How to Apply

Interested candidates should submit the following materials:

• Curriculum vitae, including a list of publications.
• Cover letter (max 1 page) including: motivation for joining our lab; why you are a strong fit; your available starting date.
• Brief research statement (max 1 page) outlining goals, proposed approach, and alignment with the chosen project.
• One selected publication you are most proud of.
• Name and contact information of at least one reference.
• Please upload all materials as a single PDF and name the file using the format:PositionX_YourFamilyName, where X is 1 or 2 (e.g., Position2_Maurizi).

Start Date: Flexible, with a preferred start in late October or November 2025.