[Hiring] Bioinformatics Machine Learning Intern @RefinedScience
Role Description
We are seeking a highly motivated Bioinformatics Machine Learning Intern to join our team. This internship is designed for Ph.D. candidates with experience applying machine learning, deep learning, or generative AI methods to single-cell omics data. You will contribute to active projects spanning single-cell biology, multiomics integration, and computational approaches to precision medicine and drug development.
Our Bioinformatics team plays a crucial role in integrating computational biology, large-scale data analysis, and machine learning to drive discoveries in precision medicine and drug development.
Key Activities
• Analyze single-cell and multiomics datasets to extract biological insights supporting precision medicine and drug development programs
• Apply and evaluate machine learning and deep learning approaches to single-cell data for tasks such as cell type classification, biomarker discovery, and patient stratification
• Explore and prototype generative AI and LLM-based approaches to accelerate biological data interpretation and scientific workflows
• Collaborate with scientists, clinicians, and data scientists to design and execute data-driven research projects
• Document and optimize computational workflows following reproducible research best practices
• Present findings through technical reports, visualizations, and presentations to cross-functional teams
Qualifications
• Current Ph.D. candidate in Bioinformatics, Computational Biology, Computer Science, Biostatistics, or a related quantitative field
• Single-cell omics experience: Demonstrated ability to process, analyze, and interpret single-cell data (scRNA-seq, scATAC-seq, CITE-seq, or spatial transcriptomics) using frameworks such as Scanpy/scverse, Seurat, or Bioconductor
• Machine learning expertise: Applied experience developing and evaluating ML/deep learning models on biological data, including neural network architectures (GNNs, transformers, autoencoders), model selection and benchmarking, and integration of ML approaches into analytical workflows
• Programming proficiency: Python and/or R for data analysis, statistical modeling, and visualization
• Statistical foundation: Understanding of statistical methods for biological data (hypothesis testing, differential expression, multiple testing correction, clustering)
• Strong problem-solving skills and ability to communicate complex insights effectively
Requirements
• Experience with deep learning frameworks (PyTorch, TensorFlow, JAX)
• Familiarity with graph neural networks, attention mechanisms, or transformer architectures applied to biological data
• Experience with ML experiment tracking and reproducibility (MLflow, Weights & Biases)
• Exposure to representation learning, variational autoencoders, or contrastive learning methods
• Familiarity with scikit-learn, XGBoost, or similar ML libraries
• Interest in or experience with LLMs, RAG systems, or agentic AI tooling
• Experience with multimodal single-cell integration (Seurat WNN, scvi-tools/MultiVI/totalVI, Muon)
• Familiarity with spatial transcriptomics analysis (Squidpy, cell2location, nf-core/spatialvi)
• Experience with cell-cell communication inference (CellChat, NicheNet, LIANA)
• Knowledge of drug-gene interaction resources (CMap/LINCS, OpenTargets, ChEMBL)
• Familiarity with Linux/Unix CLI and version control (Git/GitHub)
• Experience with containerization (Docker, Singularity) and environment management (conda, venv)
• Exposure to cloud computing platforms (GCP preferred)
• Familiarity with workflow managers (Nextflow, Snakemake)
• Adherence to best-practices for conduct reproducible computational research
Duration
• 8–10 weeks
Benefits
• Compensation: $34-$38 per hour
Company Description
At RefinedScience, our mission is to advance care by bringing together the best science, data and minds – disease by disease, patient by patient, cell by cell to discover pathways to life beyond disease.
Our Values
• Act with Purpose – We believe in rigor through deliberate and thoughtful actions
• Be Curious – Curiosity is the spark that ignites innovation and growth
• Take Ownership – True ownership leads to pride and commitment in the work we do
• Invest in Relationships – Building strong connections is the foundation for effective collaboration and trust for long term success
• Embrace Agility – We celebrate agile thinking, resilience, and adaptability
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Apply To this Job
We are seeking a highly motivated Bioinformatics Machine Learning Intern to join our team. This internship is designed for Ph.D. candidates with experience applying machine learning, deep learning, or generative AI methods to single-cell omics data. You will contribute to active projects spanning single-cell biology, multiomics integration, and computational approaches to precision medicine and drug development.
Our Bioinformatics team plays a crucial role in integrating computational biology, large-scale data analysis, and machine learning to drive discoveries in precision medicine and drug development.
Key Activities
• Analyze single-cell and multiomics datasets to extract biological insights supporting precision medicine and drug development programs
• Apply and evaluate machine learning and deep learning approaches to single-cell data for tasks such as cell type classification, biomarker discovery, and patient stratification
• Explore and prototype generative AI and LLM-based approaches to accelerate biological data interpretation and scientific workflows
• Collaborate with scientists, clinicians, and data scientists to design and execute data-driven research projects
• Document and optimize computational workflows following reproducible research best practices
• Present findings through technical reports, visualizations, and presentations to cross-functional teams
Qualifications
• Current Ph.D. candidate in Bioinformatics, Computational Biology, Computer Science, Biostatistics, or a related quantitative field
• Single-cell omics experience: Demonstrated ability to process, analyze, and interpret single-cell data (scRNA-seq, scATAC-seq, CITE-seq, or spatial transcriptomics) using frameworks such as Scanpy/scverse, Seurat, or Bioconductor
• Machine learning expertise: Applied experience developing and evaluating ML/deep learning models on biological data, including neural network architectures (GNNs, transformers, autoencoders), model selection and benchmarking, and integration of ML approaches into analytical workflows
• Programming proficiency: Python and/or R for data analysis, statistical modeling, and visualization
• Statistical foundation: Understanding of statistical methods for biological data (hypothesis testing, differential expression, multiple testing correction, clustering)
• Strong problem-solving skills and ability to communicate complex insights effectively
Requirements
• Experience with deep learning frameworks (PyTorch, TensorFlow, JAX)
• Familiarity with graph neural networks, attention mechanisms, or transformer architectures applied to biological data
• Experience with ML experiment tracking and reproducibility (MLflow, Weights & Biases)
• Exposure to representation learning, variational autoencoders, or contrastive learning methods
• Familiarity with scikit-learn, XGBoost, or similar ML libraries
• Interest in or experience with LLMs, RAG systems, or agentic AI tooling
• Experience with multimodal single-cell integration (Seurat WNN, scvi-tools/MultiVI/totalVI, Muon)
• Familiarity with spatial transcriptomics analysis (Squidpy, cell2location, nf-core/spatialvi)
• Experience with cell-cell communication inference (CellChat, NicheNet, LIANA)
• Knowledge of drug-gene interaction resources (CMap/LINCS, OpenTargets, ChEMBL)
• Familiarity with Linux/Unix CLI and version control (Git/GitHub)
• Experience with containerization (Docker, Singularity) and environment management (conda, venv)
• Exposure to cloud computing platforms (GCP preferred)
• Familiarity with workflow managers (Nextflow, Snakemake)
• Adherence to best-practices for conduct reproducible computational research
Duration
• 8–10 weeks
Benefits
• Compensation: $34-$38 per hour
Company Description
At RefinedScience, our mission is to advance care by bringing together the best science, data and minds – disease by disease, patient by patient, cell by cell to discover pathways to life beyond disease.
Our Values
• Act with Purpose – We believe in rigor through deliberate and thoughtful actions
• Be Curious – Curiosity is the spark that ignites innovation and growth
• Take Ownership – True ownership leads to pride and commitment in the work we do
• Invest in Relationships – Building strong connections is the foundation for effective collaboration and trust for long term success
• Embrace Agility – We celebrate agile thinking, resilience, and adaptability
Apply tot his job
Apply To this Job