Postdoctoral Research Fellow

About the roleThe Dubai Health Data & AI Lab is looking for a highly motivated Postdoctoral Research Fellow to lead translational research in artificial intelligence for precision medicine and digital health. The successful candidate will also contribute to a broader portfolio of multimodal healthcare AI research.You will work closely with clinicians, laboratory scientists, data scientists, engineers and external collaborators to develop rigorous, clinically relevant and reproducible AI research.Key responsibilitiesResearch LeadershipLead the day-to-day scientific execution of research across study design, cohort definition, data curation, statistical analysis, model development and validation.Translate programme objectives into testable hypotheses, analysis plans, milestones and high-quality research outputs.Maintain rigorous, version-controlled protocols, code, datasets and research documentation.Identify methodological risks, limitations, bias and opportunities for further research.AI & Machine LearningDevelop and validate predictive models and personalized treatment or dosing algorithms using Python.Build reproducible machine-learning and deep-learning pipelines using PyTorch and/or TensorFlow, scikit-learn and related open-source tools.Work with multimodal clinical and biomedical data, including EHR data, physiological signals, clinical text, medical imaging, laboratory data and omics.Apply appropriate methods for feature engineering, longitudinal modelling, multimodal learning, interpretability and uncertainty estimation.Evaluate model discrimination, calibration, clinical utility, subgroup performance, generalizability and robustness.Identify and mitigate data leakage, overfitting, confounding, selection bias, class imbalance and measurement error.Evaluate foundation models, large language models and multimodal AI approaches where scientifically justified.Clinical Validation & TranslationDesign and conduct analyses comparing AI-informed approaches with standard treatment or dosing protocols.Work with clinicians to define clinically meaningful endpoints, thresholds and safety considerations.Produce analysis plans, evidence tables, figures and validation reports for governance, ethics, publication and partner review.Contribute to model cards, requirements traceability, risk documentation, change logs and other evidence supporting progression toward clinical decision-support software or Software as a Medical Device, where applicable.Support the design of multicentre and external validation studies.Scientific Dissemination & CollaborationLead first-author publications in high-quality peer-reviewed journals and relevant international conferences.Present research at scientific meetings and contribute to competitive grant applications and intellectual-property documentation where appropriate.Collaborate with clinicians, researchers, engineers, laboratory teams, academic institutions and external technology partners.Mentor research assistants and students in Python, reproducible analysis, model evaluation and good research practice.Minimum qualificationsCompleted PhD, or PhD to be completed before the employment start date, in Artificial Intelligence, Machine Learning, Computer Science, Biomedical Engineering, Biomedical Informatics, Computational Biology, Biostatistics, Bioinformatics, Pharmacology, Pharmacometrics or a related quantitative field.At least 2 years of hands-on research experience developing and validating machine-learning or deep-learning models using Python and PyTorch and/or TensorFlow.Demonstrated research experience using clinical, biomedical or life-science datasets.Experience independently conducting research from study design and analysis through interpretation and scientific writing.At least one first-author peer-reviewed publication in a relevant journal or conference.Experience working with large datasets and at least two relevant data modalities, such as EHR data, physiological signals, medical imaging, clinical text, laboratory measurements or omics.Strong applied statistics and experimental-design skills, including cohort design, confounding awareness, bias assessment and model validation.Experience with Git and reproducible, version-controlled research workflows.Preferred qualificationsExperience with:Precision medicine, functional drug-response testing, pharmacology, pharmacometrics, pain research or clinical decision support.Multimodal learning, longitudinal modelling, foundation models or large language models.Model calibration, uncertainty estimation, subgroup analysis and external validation.Collaborating with clinicians, laboratory scientists or industry partners.SQL, experiment tracking, high-performance computing, GPU environments or cloud research platforms.Study protocols, ethics submissions, grants, regulatory documentation or SaMD-related research.Technical skillsPython, PyTorch and/or TensorFlow, scikit-learn, pandas, NumPy, SQL, Git, reproducible research pipelines, statistical modelling, model calibration, experiment tracking and scientific visualization. R is desirable but not required.What we're looking forWe are looking for an independent and rigorous researcher who combines strong hands-on AI skills with sound statistical judgement, scientific curiosity and a commitment to clinically meaningful research.The ideal candidate can take ownership of complex research from hypothesis development and multimodal data preparation through model validation, publication and translation, while working effectively across clinical, academic and technical teams.