Postdoctoral Researcher – Machine Learning and Explainable AI

OverviewUniversity of Doha for Science and Technology (UDST), officially established by Emirati 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. The university is a growing hub for research and innovation, bridging academia and industry, and supports Qatar National Vision 2030.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. Its mission is to drive scientific progress and technological innovation in intelligent systems, contributing to Qatar’s transition to a knowledge‑based economy.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. The researcher 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.Key ResponsibilitiesConduct applied research in machine learning, federated learning, and explainable AI across domains such as healthcare, energy, and smart infrastructureDevelop privacy‑preserving machine learning systems and interpretable AI solutions that ensure transparency and trustDeploy machine learning models on edge and cloud platforms, ensuring robustness and scalabilityPublish research findings in top‑tier peer‑reviewed conferences and journalsLead and contribute to high‑impact research projects aligned with the Center’s prioritiesEnsure reproducibility and documentation of research through best practices in code and data managementIntegrate XAI techniques to ensure AI solutions meet ethical standards and domain‑specific requirementsCollaborate with internal and external stakeholders on interdisciplinary research initiativesContribute to research proposals and external funding applications to support the Center’s growthMentor junior researchers, graduate students, and research assistantsRequired QualificationsPhD in Computer Science, Artificial Intelligence, Machine Learning, or a closely related fieldDemonstrated 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., 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 AIExperience with evaluating AI models, benchmark design, and assessment methodologiesExperience with explainable AI libraries (e.g., SHAP, LIME, Captum) and interpretability techniquesExperience with federated learning frameworks (e.g., Flower, TensorFlow Federated) or privacy‑preserving MLProven ability to conduct independent research and contribute to collaborative research teamsExcellent analytical, problem‑solving, and communication skills, including research presentations and technical writingProficiency with version control systems (Git) and reproducible research practicesExperience with computational resources and high‑performance computing environmentsFluency in written and spoken EnglishPreferred QualificationsExperience applying ML to healthcare, energy, or smart infrastructure domainsFamiliarity with AI safety, alignment, fairness, bias detection, or responsible AI researchExperience with cloud and edge deployment platforms (e.g., AWS SageMaker, Azure ML, Docker, Kubernetes)Knowledge of MLOps practices, CI/CD pipelines, and model deploymentExperience with model monitoring, drift detection, and retraining strategiesExperience with data engineering and preprocessing tools (e.g., Spark, Airflow, data versioning)Understanding of human‑AI interaction and user‑centered AI designFamiliarity 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 collaborationsPrevious postdoctoral or industrial research ex