PhD Studentship – Adopting Agentic Artificial Intelligence

RFCSR; Cranfield; On-site; GBP 18,000 - 20,000; Full time

Benefits offered by this job

Fully funded

Tax-free stipend

Job summary

Cranfield University offers a fully funded PhD studentship focused on designing, developing, and adopting autonomous AI agents. The project explores decision-making, collaboration, and adaptive behavior in real-world industrial contexts.

The candidate will work with supervisors, publish in leading venues, and may engage with industry partners to ensure practical impact. The programme is typically three years full-time with research dissemination opportunities.

Qualifications

• First or upper second-class honours degree (or equivalent) in computer science, AI, engineering, data science or related field.
• Master’s degree in a relevant area is desirable.
• Strong research capability and academic potential.

Responsibilities

• Conduct independent research on agentic AI and related methods.
• Publish findings in leading academic venues.
• Collaborate with supervisors and possibly industry partners for real-world relevance.
• Contribute to development of theoretical and practical AI agent frameworks.

Skills

Artificial Intelligence knowledge

Python programming

Research capability

Analytical thinking

Communication skills

Education

Bachelor's degree in CS/AI/Engineering

Master's degree desirable

Tools

Python

Algorithms

Job description

PhD Studentship – Adopting Agentic Artificial Intelligence

Cranfield University
Cranfield, United Kingdom

General Description
Cranfield University is offering a fully funded PhD studentship focused on the emerging field of Agentic Artificial Intelligence. This research project aims to explore the design, development, and adoption of autonomous AI agents capable of decision-making, collaboration, and adaptive behaviour in complex, real-world environments.

The project will investigate how agentic AI systems can be effectively integrated into organisational and industrial contexts, addressing challenges such as trust, governance, human-AI interaction, and ethical deployment. The successful candidate will contribute to advancing theoretical frameworks as well as developing practical implementations of AI agents across application domains.

The research will involve interdisciplinary approaches, combining artificial intelligence, machine learning, systems engineering, and socio-technical analysis. The candidate will work closely with academic supervisors and may engage with industry partners to ensure real-world relevance and impact. Responsibilities include conducting independent research, publishing findings in leading academic venues, and contributing to the academic community.

This studentship provides access to Cranfield’s advanced research facilities, training programmes, and a collaborative academic environment. The programme is typically three years full-time (or equivalent part-time), with opportunities for professional development and research dissemination.

Eligibility Criteria

• A first or upper second-class honours degree (or equivalent) in computer science, artificial intelligence, engineering, data science, or a related discipline
• A Master’s degree in a relevant field is desirable
• Strong academic background with evidence of research capability

Required Expertise/Skills

• Knowledge of artificial intelligence, machine learning, or autonomous systems
• Programming skills in languages such as Python or similar
• Understanding of computational modelling, algorithms, or data analysis
• Interest in interdisciplinary research, including human-AI interaction and ethical considerations
• Strong analytical, problem-solving, and critical thinking skills
• Good written and verbal communication skills
• Ability to work independently and collaboratively within a research environment

Salary Details
Fully funded studentship including tuition fees and a tax-free stipend (amount typically aligned with UKRI rates), with potential additional support for research expenses

Application Deadline
Not explicitly specified