CEDoc - UM6P - CC: Novel Machine Learning approaches for the Maritime Transportation (11566)

Mohammed VI Polytechnic University is an institution dedicated to research and innovation in Africa and aims to position itself among world-renowned universities in its fields

The University is engaged in economic and human development and puts research and innovation at the forefront of African development. A mechanism that enables it to consolidate Morocco's frontline position in these fields, in a unique partnership-based approach and boosting skills training relevant for the future of Africa.

Located in the municipality of Benguerir, in the very heart of the Green City, Mohammed VI Polytechnic University aspires to leave its mark nationally, continentally, and globally.

Context:

The maritime industry is facing increasing pressure to improve efficiency in the face of growing global demand and increasingly complex operating environments. One promising area is the development of advanced machine learning to optimize various aspects of maritime operations. However, existing approaches mainly focus on classical machine learning. Therefore, the advancement of machine learning, such as deep learning, learning on graphs, deep reinforcement learning, and automatic machine learning for the maritime industry still needs to be sufficiently investigated and benchmarked. Advanced learning algorithms facilitate intelligent decision-making in maritime applications, such as vessel trajectory mining, maritime traffic flow forecasting, shipping market modeling, etc. Moreover, maritime data are usually time-varying, high-dimensional, imperfect, non-Euclidean, or noisy, which challenges developing advanced learning algorithms.

Research objective:

The aim of this project is to develop advanced machine learning algorithms for the maritime industry. This project will be co-supervised by Profs Lamiae Azizi and Loubna Benabbou - UQAR University (Canada).

Required documents:

• Transcripts of Bachelor and Masters.
• CV
• Cover letter

Admission Criteria:

Mandatory:

• Knowledge of modern Machine Learning
• Solid Knowledge of Python
• Solid mathematical background
• Good communications skills in English

References:

1. Sara El Mekkaoui, Loubna Benabbou, Stéphane Caron, Abdelaziz Berrado (2023). Deep learning-based ship speed prediction for intelligent maritime traffic management. Journal of marine science and engineering. Vol 11, 1, 91.
2. Oussama Boussif, Ghait Boukachab, Dan Assouline, Stefano Massaroli, Tianle Yuan, Loubna Benabbou, Yoshua Bengio (2024). Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context. Advances in Neural Information Processing Systems. 36.
3. Steven YK Wong, Jennifer Chan, Lamiae Azizi, Richard Xu (2021). Supervised Temporal Autoencoder for stock return time-series forecasting. Proceedings IEEE- Computers, Software and Applications (COMPSAC).
4. Rohit Chandra, L Azizi, S Cripps (2017). Bayesian neural learning via Langevin dynamics for chaotic time series prediction. International Conference On Neural Information Processing (ICONIP).

Number of positions : 1-3

UM6P.