Anwesha Ghosh

• Researching MIMO wireless systems and 5G/6G technologies, including beamforming, channel modelling, and OFDM. • Analysing SINR, CQI, and throughput metrics for antenna configuration and wireless reliability optimisation. • Conducting simulation-based studies aligned with 3GPP communication standards.

• Working on the DARKSOL research project on distributed cloud and edge computing systems. • Supporting Kubernetes orchestration, cloud-edge computing, and containerised infrastructure deployments. • Contributing to system architecture analysis, deployment workflows, and technical documentation for research prototypes. • Working with Linux environments, networking concepts, and cloud-native technologies for scalable system management.

• Designed and tested RISC-V–based communication subsystems, integrating signal sampling, filtering, and timing synchronization • Prototyped Linux-based wireless data acquisition pipelines using C and Python • Improved data throughput stability by ~20% through debugging and interface optimizatio • Collaborated with hardware teams using oscilloscopes and logic analyzers for real-time system validation

• Managed Kubernetes deployments using Helm and implemented AWS Lambda for serverless workloads. • Implemented monitoring using Prometheus, Grafana, Datadog, CloudWatch, ELK, and Azure Monitor. • Performed performance tuning and capacity planning using Terraform, CloudFormation, and Chef. • Enforced governance policies across ECR, DockerHub, ACR, and GitHub. • Applied Kubernetes best practices using RBAC, Service Accounts, ConfigMaps, and Secrets. • Implemented build and test automation pipelines using Mave

• Analyzed system requirements and proposed efficient solutions using power electronics. • Conducted testing and validation of automation systems. • Provided technical support and training on automation technologies.

Master’s Thesis: ML-based Network- and Resource-Aware Scheduling of Dockerized Applications on Kubernetes in Distributed Cloud Systems