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  Expressions of Interest for PhD positions in I-Form, the SFI Research Centre for Advanced Manufacturing

   Centre for Doctoral Training (CDT)

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  Miss Angela Evans  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Expressions of Interest for PhD positions in I-Form, the SFI Research Centre for Advanced Manufacturing

Submit an Expression of Interest!

I-Form, the SFI Research Centre for Advanced Manufacturing seeks to attract outstanding students to work on cutting-edge research projects in additive manufacturing along with the digitalisation of materials processing. Amongst the funded PhD projects are those in metal additive manufacturing, multi-material printing and Artificial Intelligence applications for advanced manufacturing. Opportunities exist, under the supervision of the Centres’ academics at our seven partner academic institutions in Ireland (Dublin, Galway, Maynooth, Sligo, Waterford).

We invite expressions of interest (EOI) from applicants with a first or upper second-class honours degree in a science or engineering discipline for PhD positions in any of the topic areas listed below. Following the internal review process, successful candidates will be invited to submit an application. EOIs are welcome from suitably qualified candidates worldwide.

Please submit your Expression of Interest with a CV by email to [Email Address Removed] stating clearly in which one of the three Platform research areas (detailed below) for which you would like to submit an application.

For further information on I-Form and our core research see our website:

Platforms and Research Topics:

Platform 1: Materials Process Development and Data Capture

Objective: To capture new insights on the influence of processing variables and materials properties on end-product characteristics for additive manufacturing using novel materials.

Overview: I-Form’s AM processing research explores the printing of (a) metal alloys by laser powder bed fusion and (b) multi-materials by material jetting. Platform 1 experimentation will create valuable datasets and correlations that span material properties, process inputs, in-situ multi-sensor measurements, post-build processing data and end-product performance.

Research Topics

1.     Laser-based powder bed fusion (PBF-LB).

2.     Multi-material printing: Ink production, printing and sintering control

3.     Multi-sensor process monitoring.

4.     Porosity, microstructure, and surface morphology.

5.     Processing parameters and compositions of NiTi and Ti-alloys.

6.     Post processing for tailored metallic bulk and surface properties.

7.     Optimisation for low carbon footprint additive manufacturing

Platform 2: Materials Process and Product Modelling

Objective: To develop multi-scale, multi-physics models of additive manufacturing, that capture process-structure-property-performance relationships at full component level.

Overview: The focus of this Platform is to facilitate the sustainable (efficient in terms of energy and materials), rapid development of new products and processes by replacing physical experimentation in the development cycle with advanced predictive modelling. This is achieved through the development of multi-scale, multi-physics (MSMP) process-structure-property-performance (PSPP) models, combined with machine learning (ML) to provide digital tools for advanced manufacturing.



Research Topics

8.     Process modelling – MSMP computational fluid dynamics (CFD) for thermo-fluids and finite volume / finite element analysis (FEA) for thermo-mechanical modelling.

9.     Microstructure evolution during solidification.

10.  Crystal plasticity modelling

11.  Cellular Automata and Phase field modelling

12.  Fatigue and fracture mechanics modelling (e.g. xFEM, cohesive zone modelling)

13.  Topology and process optimisation for performance.

Platform 3: Artificial Intelligence and Data Analytics in Materials Processing

Objective: To create and apply novel Artificial Intelligence (AI) and data analysis techniques and tools that will rapidly analyse Additive Manufacturing process data to provide real time feedback and control.

Overview: This Platform will develop and integrate a range of computational tools for additive manufacturing in order to achieve enhanced processing efficiencies and to enable improvements in sustainability. The research work in this Platform will include data analysis for both AM alloy parts and multi-material printing. The role of human oversight and decision making in AI drive process understanding and control will also be researched.

Research Topics:

14.  Time series analysis.

15.  Anomaly detection.

16.  Image analysis.

17.  Bayesian optimisation.

18.  Active learning and human-in-the-loop.

19.  Deep reinforcement learning for real-time closed-loop control.

Materials Science (24)

 About the Project