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 ( 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: 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, optimised dielectric fluids) to improve cutting stability, surface integrity and dimensional accuracy.
Establishment of a real–virtual testing bench combining in‑situ monitoring, digital twins and model-based quality assessment, with AI‑supported analytics processing monitoring signals and providing real‑time feedback for model updating.
Benchmarking process parameters, quality metrics and sustainability indicators to ensure cross‑validation of the modelling approaches.
Expected Results & KPIs
A validated workflow integrating machining data, in‑situ monitoring signals and metrology outputs into a unified knowledge base (data ingestion completeness above 90%; at least 20% reduction in manual data-processing effort).
A software prototype implementing hybrid physics-based and data-driven models (prediction accuracy of at least 80% for dimensional accuracy and surface roughness; computational runtime reduced by at least 30% with respect to full physical simulations).
Predictive models for machining outcomes and early detection of process anomalies (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).
Quantified and experimentally validated surface‑integrity descriptors, including microstructural alterations and recast‑layer metrics (recast‑layer thickness variability within ±10% across repeated runs; tolerance windows compatible with ISO 13485 and medical device requirements).
Evidence‑based knowledge on advanced wire materials, coatings and dielectric conditions (cutting stability or MRR improved by at least 15% for at least one advanced configuration; wire wear or breakage frequency reduced by at least 20%).
Validation, Industrial Relevance & Secondments
Industrial Relevance: The project combines the long‑standing expertise of Università di Pisa in electrical discharge machining (discharge modelling, process monitoring, optimisation strategies and innovative dielectric‑fluid formulations), with the requirements of industrial and academic partners of the network.
Secondments & Research Mobility: Three secondments are planned, with a primary focus on the industrial experience at MMIMicro (3 months), where the researcher will work on the definition of tolerances and functional constraints for micro‑components used in robotic micro‑surgical assemblies, evaluate prototype parts and micro‑metrology tools, and validate the proposed monitoring approach on industrial equipment.
A complementary secondment at KU Leuven (2 months) will support benchmarking against the activities of DC5 and the development of shared guidelines for assessing hybrid modelling approaches. Finally, Politecnico di Milano (1 month) will provide expertise in advanced simulation and CAM for micro‑milling, enabling a direct benchmark between micro‑wire EDM and micro‑milling.
Where to apply E-mail (email hidden)
Requirements Research Field Engineering » Industrial engineering Education Level Master Degree or equivalent
Research Field Engineering » Biomedical engineering Education Level Master Degree or equivalent
Research Field Engineering » Mechanical engineering Education Level Master Degree or equivalent
Research Field Engineering » Microengineering Education Level Master Degree or equivalent
Skills/Qualifications
B. Technical Skills
B.1 Strong interest and hands‑on experience in precision and micro‑manufacturing, ideally including non‑conventional processes such as EDM or micro‑wire EDM.
B.2 Experience in physics‑based and numerical modelling (FEM, thermal or multiphysics simulation) and interest in reduced‑order or surrogate modelling.
B.3 Hands‑on experience in data‑driven modelling, machine learning, digital‑twin concepts and programming (e.g. Python or similar).
B.4 Experience with experimental setups, laboratory and measurement equipment, process monitoring and signal acquisition.
B.5 Familiarity with surface and microstructural characterisation (surface topography, SEM, metallographic analysis, recast layer assessment) is an asset.
B.6 Practical knowledge of design of experiments (DoE), statistical analysis and scientific presentation.
B.7 Awareness of data management, traceability and medical‑device quality frameworks (e.g. ISO 13485) is an asset.
C. Research Focus & Motivation
C.1 Strong interest in deterministic micromanufacturing, hybrid physics‑and data‑driven modelling, and high‑precision micro‑manufacturing in an industrial, MedTech‑oriented context.
C.2 Motivation to conduct independent doctoral research, while enrolled in the PhD programme at UniPi and meeting its requirements in a multidisciplinary international team, including hands‑on experimental work, the mandatory international secondments and the management of laboratory service activities.
C.3 Motivation to publish research findings in international journals, present results at conferences and fulfil project deliverables.
D. Communication & Language Skills
D.1 Excellent English, spoken and written (TOEFL or IELTS score, if available).
D.2 Ability to communicate research results clearly, both in writing and verbally.
D.3 Readiness to contribute to project activities, training events and dissemination efforts.
Languages ENGLISH Level Excellent Research Field Engineering » MicroengineeringEngineering » Biomedical engineeringEngineering » Mechanical engineeringEngineering » Industrial engineering
Additional Information Contract type: According to national legislation and MSCA Doctoral Networks requirements.
Remuneration: According to the Grant Agreement of the project, the total gross amount including salaries, social security contributions, taxes and other costs included in the remuneration under the employment contract for the doctoral candidate is approximately EUR 163,000 (living + mobility allowance) over the three-year period (total gross cost to the Administration).
The monthly gross salary will comprise a living allowance (approximately € 4,000/month, to be corrected with the country factor of the recruiting institution), mobility allowance (€ 710/month) and if applicable a family allowance (€ 660/month) and if applicable other allowances according to Grant Agreement of Project MicroMan4Health.
The family allowance and any other applicable allowances will be granted if
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