PhD position AI-Driven Metasurfaces for Biomedical Applications: Optimizing Polymer-Functionali[...]

Laboratory of Reactions and Process Engineering (LRGP)

Organisation/Company: Laboratory of Reactions and Process Engineering (LRGP)
Research Field: Technology » Nanotechnology, Engineering » Materials engineering, Engineering » Biomedical engineering
Researcher Profile: First Stage Researcher (R1) Positions
Country: France
Application Deadline: 30 Apr 2025 - 23:59 (Europe/Paris)
Type of Contract: Temporary
Job Status: Full-time
Is the job funded through the EU Research Framework Programme? Horizon Europe
Is the Job related to staff position within a Research Infrastructure? No

Offer Description

General information
Workplace: Nancy, France
Type of contract: PhD contract
Contract period: 36 months
Expected date of employment: October 2025
Proportion of work: Full time
Desired level of education: master’s degree in materials science, nanotechnology, biomedical engineering, or a related field; basic knowledge or previous experience in Machine Learning techniques.

Summary of the PhD project
The ability to engineer protein-surface interactions is crucial in designing next-generation biosensors and diagnostic platforms for cancer detection. Among the promising routes are metamaterials as they provide novel functionalities beyond what is possible by conventional materials in the field of sensors. In the biomedical field, the development of metasurfaces has been focused on the manipulation of electromagnetic and acoustic waves and drug delivery. In this project, a powerful AI-driven experimental approach will be adopted to design metasurfaces with targeted bio-interaction properties for cancer diagnostics.

The grafting of polymers onto surfaces plays a crucial role in tailoring interfacial properties, enabling precise control over surface energy, wettability, and biomolecular interactions. The protein-polymer interactions are typically governed by principles of thermodynamic equilibrium, steric exclusion, and electrostatics, while grafting density plays a major role in controlling protein adsorption through brush effects and volume exclusion. Additionally, surface energy regulates protein adhesion, either promoting or inhibiting their interaction depending on surface chemistry and wettability. To date, those properties are largely studied on flat, patterned, or 3D (nanoparticles, nanords, etc.) rationally designed surfaces. However, the development of metasurfaces should provide new insight in polymer-protein interaction and allow advancing beyond the current state-of-the-art in this field.

Metasurfaces, functionalized with responsive polymers, can offer a powerful means to enhance biomarker specificity, minimize biofouling, and improve detection sensitivity—all critical challenges in medical diagnostics and therapeutic monitoring. Unlike conventional surfaces, metasurfaces can be designed to manipulate chemical properties at the nanoscale. Accordingly, this project will focus on short-range order and hyperuniform model derived organization of metasurfaces, fabricated by electron beam lithography (EBL) and functionalized via “grafting-from” polymerization, offering a unique platform for controlling protein adhesion, molecular selectivity, and surface energy.

In parallel, Machine Learning (ML) techniques will be employed to bridge the gap between the characteristics of the synthesized metasurfaces and the polymer-protein interactions, as measured via different techniques within the project. Unsupervised learning techniques will allow identifying the most influencing surface characteristics to the measured interaction forces, while supervised ML techniques will be tested to map this connection and create a predictive model that will be subsequently employed for optimal metasurface designs that will enhance biomarker detection sensitivity. The most suitable techniques will depend on the quantity and quality of the obtained data, as well as on their nature. For example, the deployment of convolutional neural networks will be investigated for the analysis of the topology of the grafting surface, combined with more traditional regression techniques. Finally, an effort will be placed on including existing knowledge in the ML development pipeline to guide the learning process and include an explainability aspect in the developed models. Overall, this work will generate curated datasets linking metasurface patterning, polymer grafting, and protein adsorption, paving the way for new AI developments in nanomaterials science for health applications.

Keywords:
Pancreatic ductal adenocarcinoma (PDAC), Tumor microenvironment, Cancer-on-chip, Microfluidic systems, 3D cell culture

Work context

The PhD student will work under the supervision of Halima Alem (IJL), Dimitrios Meimaroglou (LRGP), and Veronica Piccialli (Sapienza University of Rome). Skills in AFM, XPS, contact angle measurements, and protein adsorption characterization would be a plus but not mandatory. Experience or interest in machine learning, AI-driven materials optimization, and computational modeling is a plus. The candidate should demonstrate problem-solving skills, interdisciplinary collaboration, and the ability to work in an international research environment. This PhD offers a unique opportunity to contribute to AI-enhanced biosensors for cancer diagnostics, with access to cutting-edge nanofabrication, AI modeling, and biomedical research facilities. Knowledge of English (oral and written) is mandatory and knowledge of French is an asset. As an enthusiastic researcher, you should enjoy teamwork and have a flexible approach to collaborating between different laboratories.

Constraints and risks

The position you are applying for is in a restricted regime zone (ZRR), which is a zone with regulated access within the framework of the protection of national scientific and technical potential. Therefore, your arrival will be subject to authorization by the competent authority of the Ministry of Higher Education, Research and Innovation. The selected candidate will take part in the second stage evaluation of the project, which will determine the financing of the project and, therefore, the pursuit of the recruitment.

About the hosting institutes

The Institute Jean Lamour (IJL) is a joint research unit of CNRS and Université de Lorraine. Focused on materials and processes science and engineering, it covers: materials, metallurgy, plasmas, surfaces, nanomaterials, and electronics. It regroups 183 researchers/lecturers, 91 engineers/technicians/administrative staff, 150 doctoral students, and 25 post-doctoral fellows. Partnerships exist with 150 companies and our research groups collaborate with more than 30 countries throughout the world. Its exceptional instrumental platforms are spread over 4 sites; the main one is located on Artem campus in Nancy.
The Laboratory of Research and Process Engineering (LRGP) is a leading autonomous research centre in Chemical and Process Engineering, both nationally and internationally. It hosts roughly 300 researchers working on the full spectrum of topics related to the modern challenges of chemical engineers, including energy and environment, process design and intensification techniques, biotechnology, kinetics and thermodynamics as well as product design. It is also part of the National Centre of Scientific Research, CNRS and of the University of Lorraine, UL, one of the largest French universities. Both IJL and LRGP, in collaboration with the UL, also offer dedicated administrative support for all incoming students and researchers.

Application

Applicants are invited to send a CV and cover letter together with diploma copies and associated quote, and Master 2 internship supervisor(s) reference letter: Halima Alem: halima.alem@domain, Dimitrios Meimaroglou: dimitrios.meimaroglou@domain (replace « domain » with « univ-lorraine.fr »)

Where to apply

E-mail: dimitrios.meimaroglou@univ-lorraine.fr

Requirements

Research Field: Engineering » Biomedical engineering, Education Level: Master Degree or equivalent

Research Field: Engineering » Materials engineering, Education Level: Master Degree or equivalent

Research Field: Technology » Nanotechnology, Education Level: Master Degree or equivalent

Additional Information

Work Location(s)

Number of offers available: 1
Company/Institute: Laboratory of Reactions and Process Engineering (LRGP)
Country: France
City: Nancy