Research Assistant/Research Associate/Research Fellow (Computer Science)
Nanyang Technological University · Singapore
About this role
The College of Computing and Data Science (CCDS) at Nanyang Technological University (NTU) is seeking a Research Assistant, Research Associate, or Research Fellow to conduct research in resource allocation for distributed computing. The role focuses on cache management and job scheduling for modern computing systems, including Large Language Models (LLMs). Key Responsibilities: Conduct research on resource allocation and distributed computing; design and develop algorithms for cache management and job scheduling; analyze theoretical aspects of distributed systems including optimization, scalability, and efficiency; implement and test methods through simulations or real-world experiments; and evaluate system performance using metrics such as latency, throughput, fairness, and robustness. Job Requirements: Candidates must hold a relevant degree in Computer Science or related disciplines: a Bachelor's for Research Assistant, a Master's for Research Associate, or a PhD for Research Fellow. Applicants should possess good knowledge of distributed computing, algorithm design, analysis, and optimization. Relevant research experience with a proven record is required. Candidates must be independent, highly analytical, proactive, and possess excellent teamwork and communication skills.
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A focused preparation guide, built around this job’s responsibilities and requirements.
✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01
Understand the role
What matters most
Algorithm Design for Distributed Resource Allocation
The core of this role involves designing and developing algorithms specifically for cache management and job scheduling. You must demonstrate a strong theoretical foundation in distributed systems to optimize performance metrics like latency, throughput, and fairness.
Empirical Validation of Research Methods
Beyond theoretical analysis, you are expected to implement and test your proposed methods. This requires proficiency in conducting rigorous simulations or real-world experiments to validate the efficiency and robustness of your distributed computing solutions.
Application to Modern Computing Systems
The research must be applied to contemporary challenges, specifically within the context of Large Language Models (LLMs). You should be prepared to discuss how your resource allocation strategies address the unique computational demands of modern AI infrastructure.
Likely challenges to think through
Balancing Theoretical Optimization with System Constraints
A common difficulty in distributed systems research is that an algorithm may be theoretically optimal but fail to perform under real-world constraints like network jitter or hardware limitations. Prepare by being ready to discuss how you handle trade-offs between mathematical elegance and practical implementation feasibility.
Managing Complexity in LLM Workloads
Scheduling jobs for LLMs introduces high variability in resource requirements. You may face challenges in ensuring fairness and robustness when workloads are unpredictable. Prepare by reviewing literature on dynamic resource allocation and how to maintain performance stability under fluctuating demand.
03
Practice your answers
Technical practice
Distributed System Resource Allocation
What to coverDiscuss the primary challenges in resource allocation for distributed systems, specifically regarding cache management. Address how you would balance trade-offs between system throughput and latency in a multi-tenant environment.
Job Scheduling for LLM Workloads
What to coverExplain how scheduling algorithms for Large Language Models differ from traditional batch job scheduling. Focus on the unique requirements of GPU memory management and the impact of model parallelism on scheduling efficiency.
Algorithm Design and Optimization
What to coverDescribe your approach to designing an algorithm for a resource-constrained environment. Explain how you would analyze the theoretical complexity and ensure the solution remains scalable as the number of nodes increases.
System Performance Metrics
What to coverWhen evaluating a new scheduling policy, how do you define and measure fairness versus robustness? Suggest how you would quantify these metrics in a simulation environment to ensure they reflect real-world performance.
Simulation vs. Real-world Testing
What to coverWhat are the limitations of using simulations to validate distributed system algorithms? Explain how you would design a testing framework that bridges the gap between theoretical simulation results and actual deployment on hardware.
Cache Management Strategies
What to coverCompare different cache eviction policies in the context of distributed computing. Discuss which strategies are most effective for minimizing data movement in systems handling large-scale AI model parameters.
Scalability in Distributed Systems
What to coverHow do you identify bottlenecks in a distributed system that prevent it from scaling linearly? Suggest techniques for optimizing communication overhead between nodes during job execution.
Optimization Techniques
What to coverDiscuss the application of heuristic versus exact optimization methods in resource allocation. Under what conditions would you prioritize a heuristic approach for real-time scheduling decisions?
Behavioural practice
Handling Research Challenges
What to coverDescribe a situation where your initial research hypothesis or algorithm design failed to produce the expected results. Explain the steps you took to analyze the failure and how you pivoted your approach.
Collaborative Research
What to coverProvide an example of a time you worked within a research team to solve a complex technical problem. Highlight how you communicated your findings and integrated feedback from others to improve the final outcome.
Independent Problem Solving
What to coverTell me about a time you had to work independently on a project with ambiguous requirements. How did you define your research scope and ensure your progress remained aligned with the overall project goals?
Communicating Technical Concepts
What to coverDescribe a time you had to explain a complex technical concept or research finding to a non-expert or a colleague from a different specialization. How did you ensure your message was understood and actionable?
02
Sharpen your knowledge
Topics to prepare
Distributed Systems Resource Allocation
Review theoretical frameworks for resource management in distributed environments. Focus on understanding how cache management and job scheduling impact system efficiency, scalability, and latency. This is directly relevant to the core research objective of optimizing modern computing systems.
Performance Metrics in Computing Systems
Study the mathematical and practical definitions of latency, throughput, fairness, and robustness in the context of distributed computing. Prepare to discuss how these metrics are used to evaluate the success of new scheduling or caching algorithms.
Skills to revise
Algorithm Design and Optimization
Practice designing algorithms for complex scheduling problems. Focus on trade-offs between computational complexity and system performance. This is essential for the requirement to design and develop new methods for cache and job management.
System Simulation and Evaluation
Develop proficiency in setting up simulation environments to test distributed system performance. Practice creating controlled experiments that measure the impact of specific variables on system throughput and fairness, as required by the role's testing responsibilities.
LLM Infrastructure Awareness
Research the specific resource allocation challenges posed by Large Language Models, such as memory bottlenecks and GPU scheduling requirements. This demonstrates alignment with the role's focus on modern computing systems.
04
Your preparation checklist
Prepare a research portfolio
Compile a summary of your previous research in distributed systems, specifically highlighting your experience with algorithm design, cache management, or job scheduling. Include links to published papers, technical reports, or code repositories that demonstrate your analytical capabilities.
Review distributed computing fundamentals
Refresh your knowledge on theoretical aspects of distributed systems, focusing on optimization, scalability, and efficiency metrics. Be prepared to discuss how these principles apply to modern computing environments, particularly the resource demands of Large Language Models (LLMs).
Develop a simulation or experimental walkthrough
Prepare to explain a past project where you implemented and tested a system. Focus on the methodology used for performance evaluation, specifically how you measured latency, throughput, fairness, and robustness in your experiments.
Formulate technical questions for the research team
Prepare thoughtful questions regarding the specific distributed computing challenges the CCDS team is currently tackling. Ask about their preferred simulation frameworks, the scale of the LLM workloads they are optimizing, and how they balance theoretical research with practical system implementation.
Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy
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