The purpose of this role is to support the design, implementation, and evaluation of a measurement\-informed, network\-aware static placement framework (Static NOVA) for geo\-localized multi\-cloud GPU training. The researcher will assist in collecting real\-world cloud network measurements, developing analytical cost and feasibility models, implementing optimization logic, and supporting simulation\-based evaluation for a systems research study.
Master’s degree or PhD (completed or pursuing) in Computer Science Engineering,Information Technology, Networking / Distributed Systems.
Minimum 2\+ years of Experience
Strong academic or applied background in networking research
Python (NumPy, Pandas, Matplotlib)
Networking tools: iperf3, ping, traceroute
Optimization tools or libraries (ILP solvers, OR\-Tools, PuLP)
Cloud platforms (at least one of AWS, GCP, Azure)
Prior experience with systems research, cloud computing, or network performance evaluation
Experience conducting measurement\-based experiments (e.g., benchmarking, performance profiling)
Familiarity with distributed systems concepts (latency, bandwidth, synchronization, throughput)
Experience working on simulation or analytical modeling projects
Prior publication experience (conference or journal) is a plus
Network latency vs bandwidth trade\-offs
Cloud pricing models (compute and network egress pricing)
Distributed machine learning communication patterns
Translate real\-world measurements into analytical models
Design cost and feasibility constraints
Implement static optimization logic
Strong analytical and problem\-solving skills
Ability to document methodology clearly for academic use
Job Types: Part\-time, Freelance
Contract length: 1 month
Pay: From ₹10,000\.00 per month
Work Location: Remote
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