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 » Microengineering 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 26 Oct 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 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 ( www.microman4health.eu ). 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
Manufacturing micro components as surgical tools or microfluidic devices requires high tolerances and functional quality. Quality control today is largely reactive and disconnected from the manufacturing data generated along the process chain,
making it hard to predict functional performance, to ensure yield, traceability and sustainability at the microscale. More specifically, fluid injectors are used across multiple domains, from automotive fuel injectors and paint nozzles to micro-dispensers for desktop and industrial biological and chemical analysers. In these components the final geometry and the surface quality of the machined holes critically influence functional performance, for instance in blood plasma handling. Micro-EDM die-sinking can produce such features in hard materials, but the relation between discharge conditions, cavity formation and functional behaviour is only partly understood, and production, measurement and testing information is seldom fed back into design and R&D; workflows.
Research objectives
The doctoral project aims to develop a modelling framework able to predict micro-injector performance starting from a detailed analysis of the micro-EDM die-sinking process. Physics-based modelling of discharge phenomena will be combined with compact black-box modules for subprocesses where sufficient data exist, but deterministic understanding remains limited; these modules will be progressively replaced by explicit and interpretable physical models.
Key objectives (and acquired competences):
Analysis of micro-EDM drilling of holes with precisely defined geometries and surface characteristics in micro-injectors and of the mechanisms governing cavity formation at the microscale.
Development of a hybrid physics-driven and data-driven model linking process parameters, discharge behaviour and resulting micro-features to the functional performance of micro-injectors used in blood plasma analysis.
Integration of production, measurement and testing information into R&D; and design workflows through big-data analysis and validated simulation tools,
enabling traceability between micro-EDM conditions and functional performance.
Development of a process–product data-management system generating new knowledge, triggering early-warning indicators and supporting predictive capabilities for micro-EDM micro-component manufacturing.
Establishment of a real–virtual testing bench combining in-situ monitoring, digital twins and model-based quality control, supporting component selection and enhancing the value of audits and certification steps.
Benchmarking product performance, optimal process parameters, robustness and sustainability across different micromanufacturing approaches.
Research focus
The research focuses on addressing the challenges of achieving reliable and highly controlled micro-EDM manufacturing at the scale required for high-precision micro-injectors. The project aims to understand the complex links between discharge phenomena, material removal, micro-scale geometry, surface integrity, and component performance, and to turn this understanding into predictive and data-driven strategies for process control and quality assurance. A key challenge is to bridge process monitoring, modelling, and metrology to enable more reliable and knowledge-driven manufacturing of next-generation micro-components.
Expected results
A data-integration and pre-processing workflow converting smart data into actionable knowledge, linking micro-EDM discharge parameters and monitoring signals with product measurements of micro-injector cavities (KPIs: data ingestion completeness above 90%; at least 20% reduction in manual data-processing effort).
A modelling framework explaining the fundamental mechanisms governing cavity formation, surface characteristics and micro-geometries relevant for plasma-handling performance (KPIs: prediction accuracy of at least 80% for cavity depth and diameter; surface-integrity descriptors such as recast layer and micro-cracks predicted within ±15% of measured values).
Predictive models for process outputs and failure modes, detecting deviations such as over-erosion, debris clogging or unstable discharge regimes (KPIs: early-warning detection rate of at least 75%; process repeatability improved by at least 20%; scrap rate reduced by at least 15% in controlled validation tests).
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