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 EngineeringInformation 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 Strong understanding of Network latency vs bandwidth trade-offs Cloud pricing models (compute and network egress pricing) Distributed machine learning communication patterns Ability to 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 The freelance researcher will be responsible for the following tasks Network Measurement Measure inter-provider bandwidth and latency Organize results into structured matrices for analysis Pricing & Resource Data Collection Collect GPU pricing and network egress pricing from cloud providers Document provider-specific constraints and assumptions Analytical Modelling Assist in modelling distributed training communication traffic Develop feasibility constraints based on bandwidth and synchronization limits Support compute and network cost modelling Static Placement Framework Implementation Implement a static network-aware GPU placement optimizer Integrate feasibility constraints into the optimization logic Simulation & Evaluation Run simulation scenarios across varying model sizes GPU counts and pricing conditions Compare Static NOVA against baseline placement strategies Generate plots and summaries of results Research Documentation Maintain clean reproducible code and experiment documentation Assist in preparing figures tables and methodology descriptions for a research paper Contact Detail 95661 33822 Job Types Part-time Freelance Pay From ₹10000.00 per month Benefits Flexible schedule Work from home Work Location Remote