The rapid pace of technological development results in continuous change in demand for technical skills. For companies like Arm, it is critical to maintain a workforce that can adapt to evolving technologies and associated skill requirements to maintain advantage. Across industry, skills shortages such as those seen in the semi-conductor workforce, become critical to national prosperity. Traditionally, organisations address skills gaps by recruiting externally or, to some extent, training internally but there are significant time-delays in these approaches. The UK government has launched a 1 billion pound national semiconductor strategy including funding to develop skills training. However, there has not to date been a coordinated approach to optimising skills training and developing skills pathways between job roles.
This project aims to create an AI-driven complex systems modelling framework for forecasting skill needs and providing dynamic, individualised pathways for skills acquisition within organisations using the semiconductor industry as an example application. Working closely with Arm the project will utilise data from multiple organisations within the semiconductor ecosystem to develop predictive models that identify future skills shortages and pathways for internal workforce mobility reducing reliance on external recruitment. Beyond the Arm partnership, the project will serve as a test case across other high-demand sectors such as green technology and cybersecurity. The research will be centred on the potential of utilising AI to analyse and connect educational material from academia and industry (including open-source material) to the needs of people already working in industry in order to promote lifelong learning and professional development through agile and efficient training pathways.
Objectives:
– Evaluation of skills trajectories between early career and mid-career software and hardware engineering roles in the semiconductor sector. This objective aims to evaluate how skill sets evolve over time across the industry, how skills are acquired and developed between early career and mid-career roles. This will (1) involve the analysis of historical and current skills data (2) utilise Arm’s unique knowledge, skills and attribute database and (3) draw on analyses of skills at early-career and mid-career levels from at least five organisations .
– Using skills matrices the project will identify areas where skills are saturated and where critical skills gaps exist.
– Development of shortest training pathways between job-families and identification of skill development journeys to support
– Bespoke CPD development through automated analysis of existing content tailored to individual career trajectories including University course information posted online
– Future-casting and predictive skills needs – through analysis of online information available through recruitment postings globally predicting future and emerging skills areas and creating pipelines to these areas from existing staff.
How to apply:
- Details on how to apply can be found in here: https://www.findaphd.com/phds/project/bridging-skill-gaps-with-ai-predicting-and-paving-career-pathways-for-a-future-ready-workforce-in-semiconductor-engineering/?p181608
- Applicants should also complete the online LISS-DTP Diversity Monitoring Form
- The closing date for applications is 17 February 2025
- Interviews will be held on: to be confirmed
- This project is offered as full-time or part-time
- For any queries contact Claire.1.lucas@kcl.ac.uk