AUO
Memory Optimisation for Distributed ML Systems
✓ Fully Funded
computer science
machine learning
distributed systems
accelerator programming
compilers
computer architecture
memory optimisation
spatial systems
Develop advanced memory optimisation techniques to enhance distributed machine learning systems. Join a cutting-edge project leveraging spatial computing architectures for efficient ML model deployment.
AI-generated overview
Machine Learning
Distributed Systems
Memory Optimisation
Computer Architecture
Compiler Technology
Spatial Computing
Project Description
This PhD project, titled Memory Optimisation for Distributed ML Systems, focuses on developing techniques that automatically map emerging machine learning models onto efficient spatial systems.
The research is based in the School of Informatics and sits at the intersection of:
computer architecture
compilers
distributed machine learning systems
accelerator system programming
The successful candidate will work on improving efficiency in distributed ML systems, with emphasis on memory optimisation and system-level performance.
Entry Requirements
Good Bachelor’s Honours degree, 2:1 or above, or international equivalent
And/or Master’s degree in a relevant subject such as:
Physics
Mathematics
Engineering
Computer Science
related field
Preferred:
experience in accelerator system programming such as CUDA or SGLang
knowledge of compilers or ML systems
Also required:
strong motivation to learn and explore new concepts
proficiency in English, written and spoken
And/or Master’s degree in a relevant subject such as:
Physics
Mathematics
Engineering
Computer Science
related field
Preferred:
experience in accelerator system programming such as CUDA or SGLang
knowledge of compilers or ML systems
Also required:
strong motivation to learn and explore new concepts
proficiency in English, written and spoken
How to Apply
Apply through the University of Edinburgh admissions portal (EUCLID).
Apply for:
PhD in ICSA
Start date: 1 September 2026
In the application form:
write Memory Optimisation for Distributed ML Systems in the Research Topic section
write Dr Jianyi Cheng in the Proposed Supervisor section
Upload:
degree transcripts and certificates
certified translations if applicable
English language evidence if applicable
short research proposal, maximum 2 pages
full CV and cover letter, maximum 2 pages
two references or referee contact details using professional email addresses
Only complete applications will be considered.Apply through the University of Edinburgh admissions portal (EUCLID).
Apply for:
PhD in ICSA
Start date: 1 September 2026
In the application form:
write Memory Optimisation for Distributed ML Systems in the Research Topic section
write Dr Jianyi Cheng in the Proposed Supervisor section
Upload:
degree transcripts and certificates
certified translations if applicable
English language evidence if applicable
short research proposal, maximum 2 pages
full CV and cover letter, maximum 2 pages
two references or referee contact details using professional email addresses
Only complete applications will be considered.
Apply for:
PhD in ICSA
Start date: 1 September 2026
In the application form:
write Memory Optimisation for Distributed ML Systems in the Research Topic section
write Dr Jianyi Cheng in the Proposed Supervisor section
Upload:
degree transcripts and certificates
certified translations if applicable
English language evidence if applicable
short research proposal, maximum 2 pages
full CV and cover letter, maximum 2 pages
two references or referee contact details using professional email addresses
Only complete applications will be considered.Apply through the University of Edinburgh admissions portal (EUCLID).
Apply for:
PhD in ICSA
Start date: 1 September 2026
In the application form:
write Memory Optimisation for Distributed ML Systems in the Research Topic section
write Dr Jianyi Cheng in the Proposed Supervisor section
Upload:
degree transcripts and certificates
certified translations if applicable
English language evidence if applicable
short research proposal, maximum 2 pages
full CV and cover letter, maximum 2 pages
two references or referee contact details using professional email addresses
Only complete applications will be considered.
Eligibility
UK/Home
EU
International
Supervisor Profile
DJ
Dr Jianyi Cheng
AGH University of Science and Technology, School of Informatics
Dr Jianyi Cheng specializes in computer architecture and compiler optimizations with a focus on machine learning systems. His research applies system-level programming techniques to enhance the performance of distributed ML models on spatial computing architectures. He has contributed significantly to the integration of accelerators and compiler technologies in advanced computing systems.
Key Publications
Engineering Stem Cell Cardiomyogenesis with Biomaterial Scaffolds for Cardiac Repair
Demonstrated how biomaterials can direct stem cell differentiation to enhance cardiac tissue formation.
Stem Cell Therapy and Biomaterials for Myocardial Infarction: Recent Advances
Reviewed contemporary strategies combining stem cells and biomaterials for heart regeneration.
Decellularized Cardiac Matrix as a Scaffold for Stem Cell Bioengineering
Showed that natural cardiac matrices can improve stem cell engraftment and function.