30 ott - Roma
University of Pisa
Department of Civil and Industrial Engineering
Organisation/Company University of Pisa Department Department of Civil and Industrial Engineering Research Field Engineering » Industrial engineering Engineering » Biomedical engineering Engineering » Microengineering Engineering » Mechanical engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 26 Nov 2026
- 23:59 (Europe/Rome) Country Italy Type of Contract Temporary Job Status Full-time Hours Per Week 37 Offer Starting Date 31 Dec 2026 Is the job funded through the EU Research Framework Programme? Horizon Europe
- MSCA Marie Curie Grant Agreement Number 101312169 Is the Job related to staff position within a Research Infrastructure? No
Offer Description Offer Description
Università di Pisa (UniPi) seeks a highly motivated PhD candidate for a fully funded international doctoral position within the Marie Skłodowska-Curie Doctoral Network (MSCA-DN), project MicroMan4Health: Data-Centric Micromanufacturing Platform Towards Added Value in the Health Sector ( ). The shift from process-centric to data-centric micromanufacturing constitutes a paradigm evolution in how knowledge, control and value are created in micromanufacturing platforms for the health sector.
About the Host
Università di Pisa is a leading Italian university where thousands of students are educated each year across a wide range of scientific and technological disciplines. UniPi has always aimed at the quality and innovation of teaching and research by developing a fruitful relationship with the economic and industrial sectors. The Department of Civil and Industrial Engineering (DICI)
provides full education in the field of advanced manufacturing processes and smart manufacturing systems. The DICI laboratories are equipped with state-of-the-art manufacturing facilities, including sink EDM, SEM and metallographic preparation, surface metrology, and various additive manufacturing technologies (metal and non-metal). UniPi is in Tuscany, a dynamic region with a strong industrial and technological ecosystem and a wonderful work-life balance.
Research Background: Micro-surgical instruments and miniature metallic components for minimally invasive devices require sub-100 μm dimensional accuracy, stable surface integrity and compliance with stringent certification requirements. Micro-wire EDM is one of the few processes capable of shaping hard metallic materials at this scale, but its behaviour is governed by stochastic discharge phenomena and is still described largely through empirical relations. Machining, in-line monitoring and metrology data are generated along the process chain but rarely integrated, so deviations affecting quality or robustness are detected only after the fact.
Research Focus: The research addresses the challenge of achieving deterministic and reliable micro-wire EDM manufacturing for medical micro-surgical instruments, where small variations in discharge behaviour,
wire–workpiece interaction and thermal effects can significantly affect dimensional accuracy and surface integrity. The project aims to bridge process physics, real-time monitoring and data-driven modelling to understand and predict how machining conditions translate into final component quality. A central challenge is to transform process and metrology data into actionable knowledge, enabling early detection of instabilities and more accurate, adaptive control of micromanufacturing processes.
Research Objectives: The doctoral project aims to develop an integrated modelling framework able to predict product performance in micro-wire EDM, starting from a multiscale and micro-physical analysis of the process. High-fidelity physical models will be combined with reduced-order, data-driven surrogate models describing subprocesses for which experimental data exist, but deterministic knowledge is still incomplete, with the objective of progressively replacing empirical formulations with physics-informed ones as new insights emerge.
Key Objectives & Acquired Competences
Construction of a hybrid modelling chain coupling physics-based discharge modelling with data-driven predictors, capturing wire wear, discharge dynamics and heat-affected zones and their causal impact on final component features.
Integration of machining, in-line monitoring, metrology and testing data into early R&D; and design workflows through advanced data analytics and validated simulation environments.
Development of a structured process–product data-management architecture enabling traceability, knowledge extraction and early-warning indicators for deviations affecting quality or robustness.
Assessment of advanced materials and wire configurations (coated wires, high-tensile alloys, optimis