Postdoctoral Researcher

Overview

University of Doha for Science and Technology (UDST), officially established by Emiri Decision No. 13 of 2022, is Qatar's first national applied university and the country's premier destination for academic, technical, and professional education. With more than 9,000 students and 700 staff, UDST offers over 70 bachelor's, master's, diploma, and certificate programs across its five colleges: Business, Computing & Information Technology, Engineering & Technology, Health Sciences, and General Education. In addition, UDST houses specialized training centers that serve both individuals and industry.

UDST is recognized for its student-centered learning, cutting-edge facilities, and applied, experiential approach. Our world-class faculty and researchers leverage innovative learning technologies and foster partnerships with global institutions to develop highly skilled graduates who contribute directly to Qatar's knowledge-based economy and support the goals of Qatar National Vision 2030. As a growing hub for research and innovation, UDST is home to advanced projects that bridge academia and industry.

UDST Center of Excellence - Artificial Intelligence and Innovation is dedicated to advancing cutting-edge AI research and developing practical solutions that tackle real-world challenges. Our mission is to drive scientific progress and technological innovation in intelligent systems, contributing to Qatar's transition to a knowledge-based economy in line with Qatar National Vision 2030.

We are seeking an outstanding Postdoctoral Researcher to join our team at the Center of Excellence in Artificial Intelligence and Innovation. This role focuses on conducting applied research in machine learning, federated learning, and explainable AI (XAI) with real-world applications in healthcare, energy, and smart infrastructure. You will develop privacy-preserving machine learning systems, create interpretable AI solutions, and deploy models on edge and cloud platforms. This is an excellent opportunity to advance trustworthy AI research while contributing to Qatar's innovation priorities and digital transformation.

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 Qualifications Required Qualifications Ph D 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 MLStrong publication record in peer-reviewed journals and top-tier AI/ML conferences (e.g., Neur IPS, ICML, ICLR, AAAI, CVPR, KDD) Expertise in deep learning frameworks (Py Torch, Tensor Flow, Keras) and classical ML tools (scikit-learn, XGBoost) Strong background in machine learning, federated learning, or explainable AIExperience 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, Tensor Flow Federated) or privacy-preserving MLProven 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 Preferred Qualifications 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 Sage Maker, Azure ML, Docker, Kubernetes) Knowledge of MLOps practices, CI/CD pipelines, and model deployment Experience with model monitoring, drift detection and re-training strategies Experience with data engineering and preprocessing tools (e.g., Spark, Airflow etc…) checked? Understanding of human-AI interaction and user-centered AI design Familiarity with domain-specific toolkits (e.g., MONAI for medical imaging, FHIR for healthcare) Track record of research impact (citations, h-index, awards) Experience with interdisciplinary research or industry collaborations previous job? Previous postdoctoral or industrial research experience Experience with grant writing and research funding applications Contributions to open-source AI projects or frameworks Arabic language skills What 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

Application Deadline: 31st March 2026

UDST is an equal opportunity employer committed to building a diverse and inclusive workforce. We welcome applications from all qualified candidates regardless of race, color, religion, gender, national origin, age, disability, or veteran status.

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