Postdoctoral Researcher – Machine Learning and Explainable AI

Overview

Key Responsibilities

• Conduct applied research in machine learning, federated learning, and explainable AI across domains such as healthcare, energy, and smart infrastructure
• Develop privacy-preserving machine learning systems and interpretable AI solutions that ensure transparency and trust
• Deploy machine learning models on edge and cloud platforms, ensuring robustness and scalability
• Publish research findings in top-tier peer-reviewed conferences and journals
• Lead and contribute to high-impact research projects aligned with the Center's priorities
• Ensure reproducibility and documentation of research through best practices in code and data management
• Integrate XAI techniques to ensure AI solutions meet ethical standards and domain-specific requirements
• Collaborate with internal and external stakeholders on interdisciplinary research initiatives
• Contribute to research proposals and external funding applications to support the Center's growth
• Mentor junior researchers, graduate students, and research assistants

Required Qualifications

• PhD in Computer Science, Artificial Intelligence, Machine Learning, or a closely related field
• Demonstrated research experience in machine learning, with focus on federated learning, explainable AI, or privacy-preserving ML
• Strong publication record in peer-reviewed journals and top-tier AI/ML conferences (e.g., NeurIPS, ICML, ICLR, AAAI, CVPR, KDD)
• Expertise in deep learning frameworks (PyTorch, TensorFlow, Keras) and classical ML tools (scikit-learn, XGBoost)
• Strong background in machine learning, federated learning, or explainable AI
• Experience with evaluating AI models, benchmark design, and assessment methodologies
• Experience with explainable AI libraries (e.g., SHAP, LIME, Captum) and interpretability techniques
• Experience with federated learning frameworks (e.g., Flower, TensorFlow Federated) or privacy-preserving ML
• Proven ability to conduct independent research and contribute to collaborative research teams
• Excellent analytical, problem-solving, and communication skills, including research presentations and technical writing
• Proficiency with version control systems (Git) and reproducible research practices
• Experience with computational resources and high-performance computing environments
• Fluency in written and spoken English

How to Apply

• Experience applying ML to healthcare, energy, or smart infrastructure domains
• Familiarity with AI safety, alignment, fairness, bias detection, or responsible AI research
• Experience with cloud and edge deployment platforms (e.g., AWS SageMaker, Azure ML, Docker, Kubernetes)
• Knowledge of MLOps practices, CI/CD pipelines, and model deployment
• Experience with model monitoring, drift detection, and retraining strategies
• Experience with data engineering and preprocessing tools (e.g., Spark, Airflow, data versioning)
• Understanding of human-AI interaction and user-centered AI design
• Familiarity with domain-specific toolkits (e.g., MONAI for medical imaging, FHIR for healthcare data)
• Track record of research impact (citations, h-index, awards)
• Experience with interdisciplinary research or industry collaborations
• Previous postdoctoral or industrial research experience
• Experience with grant writing and research funding applications
• Contributions to open-source AI projects or frameworks
• Arabic language skillsWhat We Offer
• + Competitive salary and comprehensive benefits package
• + Opportunity to conduct applied research in ML, federated learning, and explainable AI with real-world impact in healthcare and energy
• + Collaborative and dynamic work environment with cutting-edge technology
• + Access to state-of-the-art facilities and computational resources through strategic partnerships
• + Professional development opportunities, international collaborations, and conference participation

Interested Candidates Should Submit The Following Documents

• Updated CV/Resume
• Cover letter highlighting research experience, interests, and motivation
• List of publications and research statement (required)
• Contact information for two professional references

Application Deadline: 31st March 2026