RefinedScience - Bioinformatics Machine Learning Intern

• 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. • Analyze single-cell and multiomics datasets to extract biological insights supporting precision medicine and drug development programs • 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 • 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 • Machine Learning & AI • 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 • Bioinformatics • 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) • Engineering & Infrastructure • 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