Postdoctoral Researcher – Machine Learning and Explainable AI

About University of Doha for Science and Technology (UDST)

UDST, established by Emiri Decision No. 13 of 2022, is Qatar's first national applied university and a leading institution for academic, technical, and professional education. Serving over 9,000 students with a staff of 700, UDST offers more than 70 programs ranging from bachelor's to certificate levels across five colleges: Business, Computing & Information Technology, Engineering & Technology, Health Sciences, and General Education. It also hosts specialized training centers catering to individuals and industry needs.

Recognized for student-focused learning, state-of-the-art infrastructure, and an applied experiential learning approach, UDST leverages advanced technologies and global partnerships to prepare graduates who contribute directly to Qatar's knowledge economy and support Qatar National Vision 2030. The university serves as a growing center for research and innovation that bridges academic inquiry with industry challenges.

Center of Excellence in Artificial Intelligence and Innovation

This center advances forefront AI research and develops practical solutions to real-world problems. Its mission aligns with Qatar's transition to a knowledge-based economy, promoting scientific and technological innovation in intelligent systems.

Role Overview

We invite applications for a postdoctoral researcher position to join the AI and Innovation center. The role involves applied research in machine learning, federated learning, and explainable AI (XAI), with applications in healthcare, energy, and smart infrastructure. Key tasks include developing privacy-aware machine learning systems, interpretable AI solutions, and deploying models on edge and cloud platforms. This opportunity supports trustworthy AI advancements contributing to Qatar’s digital transformation goals.

Primary Responsibilities

• Lead applied research in machine learning, federated learning, and XAI targeting sectors such as healthcare, energy, and infrastructure.
• Design privacy-preserving and transparent AI systems ensuring interpretability and trustworthiness.
• Implement and maintain machine learning models on cloud and edge computing environments, focusing on scalability and robustness.
• Publish research outcomes in prominent peer-reviewed journals and conferences.
• Guide and engage in impactful projects aligned with center priorities.
• Ensure reproducibility and documentation adhering to best coding and data management practices.
• Integrate ethical and domain-specific requirements using XAI methodologies.
• Collaborate with diverse stakeholders on interdisciplinary research undertakings.
• Contribute to drafting research proposals and securing external funding.
• Mentor junior researchers, graduate students, and assistants.

Required Qualifications

• PhD in Computer Science, AI, Machine Learning, or a closely related discipline.
• Proven research track record in areas including federated learning, explainable AI, or privacy-preserving ML.
• Strong publication history in top AI/ML venues (e.g., NeurIPS, ICML, ICLR, AAAI, CVPR, KDD).
• Proficiency with deep learning platforms such as PyTorch, TensorFlow, and Keras, along with classical ML tools like scikit-learn and XGBoost.
• Expertise in designing and evaluating ML and federated learning models and assessments.
• Hands-on experience with XAI frameworks like SHAP, LIME, or Captum.
• Familiarity with federated learning platforms including Flower or TensorFlow Federated.
• Ability to conduct independent and collaborative research projects.
• Excellent analytical, communication, and technical writing skills in English.
• Knowledge of version control (Git) and reproducible research methodologies.
• Experience using high-performance computing and computational resources.

Preferred Skills and Experience

• Application of ML techniques to healthcare, energy, or smart infrastructure sectors.
• Understanding of AI safety, fairness, bias mitigation, and responsible AI practices.
• Experience deploying models on cloud and edge platforms (AWS SageMaker, Azure ML, Docker, Kubernetes).
• Knowledge of MLOps tools, CI/CD pipelines, and model operational monitoring.
• Expertise in data engineering pipelines (Spark, Airflow, data versioning).
• Insight into human-AI interactions and user-centric AI designs.
• Familiarity with specialized toolkits like MONAI for medical imaging or FHIR for healthcare data.
• Demonstrated research impact through citations, h-index, or awards.
• Experience in interdisciplinary teamwork and industry partnerships.
• Background in postdoctoral or industrial research environments.
• Grant writing and funding acquisition experience.
• Contributions to open-source AI projects.
• Arabic language proficiency.

Benefits and Opportunities

• Competitive compensation and a comprehensive benefits package.
• Engagement in challenging, impactful AI research projects with real-world applications.
• Access to innovative technology, modern research infrastructure, and computational resources.
• Collaborative workplace fostering professional growth and international collaboration, including conference participation.

Application Instructions

• Submit current curriculum vitae or resume.
• Include a cover letter outlining research background, interests, and motivation for this role.
• Provide a list of publications and a detailed research statement.
• Provide contact details for two professional referees.

Additional Information

Application deadline is 31st March 2026. UDST is an equal opportunity employer dedicated to diversity and inclusion, encouraging candidates from all backgrounds regardless of race, religion, gender, national origin, disability, or veteran status.