Job Description

Design, implement, and evaluate intelligent models for communication system optimization and fault prediction using hybrid deep learning architectures. Develop reproducible, data-driven frameworks to enhance reliability, scalability, and performance in next-generation communication networks.

Lead the design and simulation of deep learning-based frameworks for network performance enhancement and failure prediction.

Develop Python-based modules integrating time-series, spatial, and environmental data for predictive communication analytics.

Conduct simulation-driven experiments using benchmark datasets, realistic channel/noise models, and communication workloads.

Publish results, including model architecture, evaluation metrics, and interpretability analysis, in peer-reviewed journals.

Master's or Ph.D. in Electronics and Communication Engineering (ECE), Communication Systems or related fields.

Strong background in wireless communication systems, signal processing, and machine learning.

Proficiency with Python and deep learning frameworks such as TensorFlow and Keras.

Programming:

Python (mandatory)

Communication Frameworks:

Knowledge of 5G/6G system models, link-level simulators, or network-level performance tools.

Minimum 2+ years of research or development experience in wireless communication systems, machine learning, or data-driven network analysis.

Hands-on experience in LSTM, CNN, Attention, or hybrid deep learning architectures.

Proven record of conducting and publishing reproducible research with strong experimental design and analysis skills.

Deep understanding of radio communication principles, signal quality metrics (RSRP, SINR, RSRQ), and link-level modelling.

Expertise in data-driven modelling using time-series and spatio-temporal datasets.

Familiarity with evaluation metrics such as accuracy, F1-score, G-mean, and ROC-AUC.

Ability to interpret deep learning models (e.g., using SHAP, attention maps) for explainability.

Strong analytical and programming skills for implementing scalable and interpretable models.

Develop, simulate, and validate intelligent frameworks for reliable communication system performance prediction.

Implement hybrid deep learning architectures (e.g., LSTM, Attention, ConvLSTM) in Python for analysing communication datasets.

Conduct experiments using benchmark datasets (e.g., ITU AI/ML in 5G Challenge) and realistic communication parameters.

Benchmark proposed models against traditional and state-of-the-art baselines, analysing performance under dynamic environmental conditions.

Ensure originality, reproducibility, and academic integrity across all research stages.

Contact : 95661 33822

Job Types: Part-time, Freelance, Volunteer
Contract length: 1 month

Pay: From ?10,000.00 per month

Work Location: Remote