机器学习运维(MLOps)稳健性风险管理博士研究员

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Postgraduate research project

Risk management for robust Machine Learning Operations (MLOps)

Funding

Competition funded

Competition funded

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Type of degree

Doctor of Philosophy

Entry requirements

2:1 honours degree

2:1 honours degree

View full entry requirements

Faculty graduate school

Faculty of Engineering and Physical Sciences

Closing date

4 Dec 2026

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About the project

Potential supervisors

Entry requirements

Fees and funding

How to apply

Contact us

About the project

Potential supervisors

Entry requirements

Fees and funding

How to apply

Contact us

About the project

Machine Learning Operations (MLOps) pipelines integrate data, model development, deployment, monitoring, and operational processes to maintain machine learning systems in production. As these pipelines become increasingly complex and interconnected, their robustness under uncertainty, adversarial conditions, and distributional shifts is critical. This project develops a risk-oriented framework to systematically assess and manage MLOps robustness.

MLOps refers to the processes for developing and maintaining machine learning systems. Robust MLOps ensure reliability under uncertainty, adversarial conditions, and distributional shifts. Given the massive growth of ML-based projects across scientific fields, effectively managing risks to MLOps robustness has become non-negotiable.

The growing adoption of MLOps frameworks for deploying ML systems in production has introduced complex, dynamic, and interconnected software supply chains. In this context, robustness, that is, the ability of systems to perform reliably under uncertainty, adversarial conditions, and distributional shifts, has become a defining criterion for trustworthy AI. As stated by Bayram and Ahmed (2025), building robust MLOps systems requires embedding robustness considerations across all processes:

• automation of operations (validation, versioning, monitoring, updates)

• dataOps (data cleaning, addressing distribution shifts and data scarcity, resource scheduling)

• modelOps (hyperparameter optimisation, model generalisability, coping with concept drift and label noise)

However, current MLOps practices lack tools and methodologies to systematically evaluate and manage risks to these processes, leaving production systems fragile, opaque, and difficult to assure throughout their life cycle. These limitations not only undermine trust and safety but also lead to operational inefficiencies, technical debt, and compliance challenges, particularly in high-stakes sectors such as healthcare, finance, and autonomous systems.

This project addresses the gap in assessing risk to MLOps robustness. It aims to operationalise robustness in MLOps through a risk-oriented framework, guided by the following research questions:

• how can the robustness of MLOps pipelines be systematically assessed?

• how can risks to the robustness of MLOps pipelines be systematically managed?

The School of Electronics and Computer Science is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.

Potential supervisors

Lead supervisor

Dr Leonardo Aniello

Associate Professor

Research interests

• Blockchain-based Systems

• Distributed Systems

• Machine Learning applied to Cyber Security

Entry requirements

You must have a UK 2:1 honours degree or its international equivalent.

Fees and funding

We offer a range of funding opportunities for both UK and international students. Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students.

Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants.

Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs for top-ranked applicants.

Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.

For more information, please visit our postgraduate research funding pages.

How to apply

Apply now

You need to:

• choose programme type (Research), 2027/28, Faculty of Engineering and Physical Sciences

• select Full time or Part time

• search for programme PhD Computer Science (7089)

• add name of the supervisor in section 2 of the application

Applications should include:

• research proposal

• your CV (resumé)

• 2 academic references

• degree transcripts and certificates to date

• English language qualification (if applicable)

Contact us

Faculty of Engineering and Physical Sciences

For questions about applying, email our Doctoral College (doctoralcollege-admissions@soton.ac.uk).

Project leader

For an initial conversation, email Dr Leonardo Aniello (l.aniello@soton.ac.uk).