University Health Network
Healthcare
PostdoctoralResearcher
“Postdoctoral Researcher at University Health Network. Skills: AI/ML, foundation models, deep learning, transfer learning, Python, PyTorch, Hugging Face, biomedical data, distributed analysis, privacy-preserving analysis, federated learning. Design and implement multimodal drug foundation models that integrate molecular graph representations, bulk transcriptomic perturbation signatures, and multi-omics cell-state representations. Develop transfer learning strategies to support diverse drug predic”
What You'll Achieve.
build a general-purpose drug foundation model capable of transfer learning across diverse prediction tasks; develop and apply secure, scalable, and privacy-preserving computational infrastructure to support biomarker discovery across diverse treatment modalities; advance AI-driven drug discovery, biomarker development, and clinical translation
Industry & Context.
Criminal Record Check may be required
What They're Looking For.
Must Have
PhD within the previous 5 years, or an MD or DDS within the previous 10 years in a relevant quantitative or biomedical discipline, including but not limited to: Computational Biology, Bioinformatics, Systems Biology, or Quantitative Machine Learning, Artificial Intelligence, Computer Science, or Data Biostatistics, Biomedical Engineering, or related fields, Demonstrated experience developing or applying deep learning methods to molecular, biological, clinical, or multi-omics data, Expertise in one or more modern AI/ML approaches relevant to foundation models or representation learning, such as graph neural networks, transformers, generative models, self-supervised learning, few-shot or zero-shot learning, or transfer learning, programming skills in Python and/or R, practical experience using modern machine learning frameworks and tooling such as PyTorch, Hugging Face, PyTorch Geometric, Deep Graph Library, scikit-learn, or equivalent platforms, Experience working with large-scale biomedical datasets, such as molecular graphs, transcriptomic perturbation profiles, multi-omics data, clinical genomics, electronic health record-derived data, or treatment-response datasets, Proficiency with reproducible workflow management systems such as Snakemake, Nextflow, CWL, or equivalent pipeline frameworks, Familiarity with cloud or high-performance computing environments, such as GCP, AWS, SLURM-based clusters, or equivalent infrastructure
Nice to Have
Understanding of data harmonization, privacy-preserving analysis, federated learning, secure distributed computing, or clinical data governance is highly desirable, publication record, commensurate with career stage, in computational biology, AI/ML, bioinformatics, biostatistics, biomedical data science, or related fields, Excellent communication skills and ability to work collaboratively in interdisciplinary teams spanning computational biology, machine learning, software engineering, oncology, and clinical research, Experience with clinical data harmonization (e. g. , OMOP CDM, HL7 FHIR) is preferred, Experience designing scalable bioinformatics pipelines for large-scale genomic or transcriptomic datasets, preferred, Understanding of regulatory and data governance requirements in clinical research settings, preferred, Prior collaborative work across computational and clinical or wet-lab research teams, preferred
What You'll Do.
Design and implement multimodal drug foundation models that integrate molecular graph representations
bulk transcriptomic perturbation signatures
and multi-omics cell-state representations
Develop transfer learning strategies to support diverse drug prediction tasks
including mechanism of action classification
clinical drug response prediction
ADMET and toxicity profiling
combinatorial drug synergy
Build flexible AI/ML workflows that reduce reliance on task-specific architectures and enable generalization across therapeutic contexts
and treatment modalities
Architect and deploy secure
and privacy-preserving computational infrastructure for biomarker discovery and translational cancer research
Develop agentic AI-enabled frameworks to support the harmonization
and integration of clinical
and transcriptomic data across public cohorts and private institutional datasets
Implement distributed and federated analysis pipelines in which each contributing dataset can be analysed separately
enabling multi-cohort biomarker assessment without raw data centralization
Develop systematic workflows to evaluate published and user-specified DNA and RNA signatures
and treatment-response signatures
Assess the predictive value of molecular signatures across cancer types and treatment modalities
including chemotherapy
and emerging therapeutic approaches
Integrate biomarker discovery and immunotherapy inference pipelines with clinical data warehouses to support translational studies in collaboration with clinical
and computational partners
Contribute to responsible data sharing frameworks
data governance processes
and regulatory documentation as required
Collaborate closely with computational biologists
and wet-lab scientists to build
and translate predictive models and biomarker discovery workflows
How You'll Work.
Team & Collaboration
Collaborate closely with computational biologists, software developers, clinicians, and wet-lab scientists; work collaboratively in interdisciplinary teams spanning computational biology, machine learning, software engineering, oncology, and clinical research
Communication Scope
Excellent communication skills
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