AU Ullah

This PhD project at Edinburgh Napier University, within the School of Computing, Engineering & the Built Environment, addresses the integration of DevOps practices into machine learning (ML)-driven systems. The operationalisation of ML, known as MLOps, faces ongoing challenges in reproducibility, scalability, monitoring, and collaboration between data science and engineering teams. While DevOps has established principles for continuous integration, delivery, and deployment (CI/CD) in traditional software engineering, its application to the ML lifecycle is still evolving. The project aims to bridge this gap by developing a systematic framework that adapts DevOps methodologies to the unique requirements of ML systems. The research will focus on unifying DevOps and MLOps practices into a coherent lifecycle for continuous ML development, proposing a maturity model to assess organisational readiness and capability, and validating the framework through case studies and experimental prototypes. Applicants should have a strong background in computer science, software engineering, programming, and machine learning, with knowledge of DevOps and MLOps principles. Good writing and communication skills are essential, and statistics skills are desirable. The studentship covers full UK or international tuition fees and provides a standard living allowance at the RCUK rate (£21,383 per annum, subject to annual increases). International applicants must cover their own visa and NHS surcharge costs. The application process requires a completed form, CV, two academic references, a two-page research outline, a one-page motivation statement, and evidence of English proficiency if applicable. The project code 'SCEBE1125' and the advertised title must be used. The deadline for applications is 9 January 2026, with the studentship starting in October 2026. For informal enquiries, contact Dr SR Rafi at [email protected].

This PhD project at Edinburgh Napier University, within the School of Computing, Engineering & the Built Environment, investigates the impact of generative AI on human well-being across diverse professional sectors. While generative AI offers significant productivity gains, it also introduces new challenges such as increased performance expectations, reduced autonomy, and career uncertainty, particularly in AI-augmented environments. The project highlights sector-specific concerns: in education, generative tools may erode teachers’ professional judgment and empathy; in IT, coding assistants can diminish creative ownership and intensify deadline pressure; and in healthcare, AI diagnostics may undermine clinicians’ trust in their expertise and accountability. Additionally, the reuse of open-source content by large language models (LLMs) raises ethical and emotional issues related to authorship and the value of creative work. Organisationally, the rapid adoption of generative AI by large companies like Amazon sets aggressive industry benchmarks, while smaller organisations often lack the resources and governance structures to implement AI responsibly, potentially widening capability gaps and amplifying workplace stress and inequity. The proposed research aims to develop a human-centred, evidence-informed framework to identify and mitigate well-being risks associated with generative AI adoption. Key objectives include mapping risk pathways, conceptualising well-being indicators, and articulating governance patterns that balance innovation with dignity, autonomy, and fairness. The project moves beyond technical and efficiency-driven narratives to establish well-being assurance as a core principle in the future of AI-enabled work and society. Applicants should have a first degree (minimum 2:1) in Computer Science, experience in software engineering, programming skills, knowledge of machine learning, and strong communication abilities. Knowledge of statistics is desirable. English proficiency (IELTS 6.5 overall, minimum 6.0 in each component) is required. The studentship covers full UK or international tuition fees and provides a standard living allowance at the RCUK rate (£21,383 per annum), but international applicants must cover visa and NHS surcharge costs. The application process requires a completed form, CV, two academic references, a two-page research outline, a one-page motivation statement, and evidence of English proficiency. The project code 'SCEBE1125' and the advertised title must be used. The studentship starts in October 2026, with a deadline of January 9, 2026. For informal enquiries, contact Dr SR Rafi at [email protected].