Dirección- Design and develop models to generate realistic Network Data, with high fidelity (e.g., capture spatio-temporal correlations in multivariate time series data, or correlations in tabular Network Data).
- Conduct experiments to evaluate the accuracy and fidelity of Network Data generation models.
- Implement state-of-the-art techniques such as differential privacy and homomorphic encryption to ensure user privacy.
- Stay up to date with industry trends and developments related to generative deep learning models and make recommendations for future improvements.
- Write technical reports and documentation to communicate results to internal and external stakeholders.
Qualifications :
- PhD (or close to completion) in Computer Science, Electrical Engineering, or related field.
- Very good record of research output (e.g., publications in top conferences and journals).
- Strong experience in data science, and machine learning applied to networks, and ideally some experience with generative deep learning.
- Knowledge of Network Data characteristics, networking architecture, traffic models.
- Strong programming skills in languages such as Python, and Deep Learning libraries such as PyTorch and TensorFlow.
- Experience with large-scale data processing tools (e.g., Spark) is a plus.
- Excellent analytical and problem-solving skills.
- Strong communication and interpersonal skills to collaborate with cross-functional teams.
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