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- Tasks: Join a cutting-edge research team to develop innovative AI systems and network protocols.
- Company: Brunel University London, a leader in research and innovation.
- Benefits: Generous salary, waived tuition fees, hybrid working, and extensive training opportunities.
- Other info: Inclusive environment with excellent career growth and support services.
- Why this job: Make a real impact in AI research while earning a fully funded PhD.
- Qualifications: Background in computer science, machine learning, or related fields required.
The predicted salary is between 40757 - 40757 £ per year.
This PhD scholarship is part of the EU Horizon Europe Marie Skłodowska‑Curie Actions Doctoral Network (MSCA DN) project ANT – Embedded AI Systems and Applications.
Location: Brunel University of London, Uxbridge Campus
Salary: £40,757 per annum inclusive of London Allowance. The university will waive international PhD tuition fees for the duration of the program.
Contract Type: Fixed‑term for 29 months, followed by a 7‑month stipend at the UKRI rate (approximately £1,983.75 per month).
Research Scope: Project ANT offers 18 fully funded PhD positions aimed at advancing research in embedded and networked AI systems.
Responsibilities:
- Develop networked scalable learning techniques that accommodate dynamic topology, computing loads, data volume, resource availability, and quality of service (QoS) requirements.
- Design network protocols to facilitate networked scalable learning.
- Develop optimisation schemes for dynamic resource allocation and computing load distribution.
Candidate Profile: Candidates should have a background in computer science, machine learning, electrical engineering, telecommunication engineering, applied mathematics, or related areas.
Desirable Skills / Interests: Machine learning, large language models, signal processing, wireless communications, applied optimisation.
Secondments:
- ST (3 months, M16‑M18, may be rescheduled): Networked scalable and continual learning in dynamic evolving environment.
- IMEC (4 months, M27‑M30, may be rescheduled): Efficient data transfer protocols for networked scalable learning.
Benefits:
- Annual leave package plus discretionary university closure days.
- Excellent training and development opportunities.
- Occupational pension scheme.
- Family allowance may be payable on eligibility and supporting evidence.
- Health‑related support services.
- Hybrid working approach.
- Research training and network fees available up to £50,139.84 for 3 years.
Contact: For an informal discussion, please email Professor Kezhi Wang at Kezhi.Wang@brunel.ac.uk.
The University is fully committed to creating and sustaining an inclusive workforce. We welcome applicants from all backgrounds and communities, especially those currently under‑represented in our workforce.
Research Assistant (MSCA) with PhD Full Scholarship - 16044 in Uxbridge employer: Brunel University of London
Contact Detail:
Brunel University of London Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Research Assistant (MSCA) with PhD Full Scholarship - 16044 in Uxbridge
✨Tip Number 1
Network like a pro! Reach out to current or former PhD students at Brunel University, especially those involved in the ANT project. A friendly chat can give us insider info and maybe even a referral!
✨Tip Number 2
Prepare for that interview! Brush up on your knowledge of networked scalable learning and AI systems. We want to show them we’re not just passionate but also knowledgeable about the field.
✨Tip Number 3
Showcase your skills! If you’ve worked on relevant projects or have experience in machine learning or optimisation, make sure to highlight these during discussions. We need to demonstrate how we can contribute to their research goals.
✨Tip Number 4
Apply through our website! It’s the best way to ensure our application gets seen. Plus, it shows we’re serious about joining the team at Brunel University.
We think you need these skills to ace Research Assistant (MSCA) with PhD Full Scholarship - 16044 in Uxbridge
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your application to highlight how your background in computer science or related fields aligns with the research scope of the project. We want to see how your skills fit into our vision for advancing embedded AI systems!
Showcase Relevant Experience: Don’t forget to include any relevant projects or experiences that demonstrate your expertise in machine learning, optimisation, or network protocols. We love seeing practical examples of your work that relate to the responsibilities outlined in the job description.
Be Clear and Concise: Keep your application clear and to the point. We appreciate well-structured applications that make it easy for us to see your qualifications and enthusiasm for the role. Avoid jargon unless it’s necessary to showcase your expertise!
Apply Through Our Website: Remember to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for this exciting opportunity. We can’t wait to hear from you!
How to prepare for a job interview at Brunel University of London
✨Know Your Research
Dive deep into the specifics of the ANT project and its focus on embedded AI systems. Familiarise yourself with the latest advancements in networked scalable learning techniques, as well as the challenges in dynamic environments. This will show your genuine interest and understanding of the role.
✨Showcase Relevant Skills
Prepare to discuss your background in computer science, machine learning, or related fields. Highlight any projects or experiences that align with the responsibilities listed, such as designing network protocols or developing optimisation schemes. Be ready to provide examples that demonstrate your expertise.
✨Ask Insightful Questions
Prepare thoughtful questions about the research scope, secondments, and training opportunities. This not only shows your enthusiasm but also helps you gauge if the position aligns with your career goals. Consider asking about the collaborative aspects of the project and how it integrates with other research initiatives.
✨Emphasise Adaptability
Given the dynamic nature of the role, be prepared to discuss how you've adapted to changing circumstances in past projects. Share examples of how you've successfully navigated challenges in resource allocation or computing load distribution, showcasing your problem-solving skills and flexibility.