Remote | Computational Biologist (Single-Cell Genomics) — $60–$90/hour

We are sharing a specialised part-time consulting opportunity for computational biologists and bioinformatics professionals with graduate-level expertise in single-cell genomics, scientific programming, and advanced computational analysis. This role supports a research project focused on challenging computational biology problems grounded in real scientific workflows. Selected experts will design original graduate-level tasks involving single-cell RNA sequencing, trajectory inference, spatial transcriptomics, multi-omic analysis, and related computational methods, then develop reference solutions and refine tasks through iterative testing. Key Responsibilities Computational Biology Problem Design Create original graduate-level computational problems in bioinformatics and single-cell genomics Develop tasks based on realistic research workflows and biological datasets Design multi-step problems requiring scientific interpretation and computational reasoning Create reproducible tasks with clearly defined inputs, expected outputs, and validation criteria Refine task difficulty based on testing and feedback Single-Cell RNA-Seq Analysis Develop problems involving preprocessing, quality control, dimensionality reduction, clustering, and downstream analysis Design workflows using tools such as Scanpy and related single-cell Python libraries Create tasks involving cell-type annotation and biological interpretation Evaluate analytical choices, parameter settings, and potential failure modes Incorporate realistic edge cases encountered in single-cell datasets Trajectory & Dynamic Analysis Create computational problems involving pseudotime and cellular trajectory inference Apply tools such as scVelo to RNA velocity and dynamic-state analysis Develop tasks requiring interpretation of lineage relationships and cell-state transitions Evaluate assumptions underlying trajectory and velocity models Design problems where multiple plausible biological interpretations must be distinguished through careful analysis Spatial Transcriptomics Develop problems involving spatially resolved gene-expression data Work with tools such as Squidpy and related spatial-analysis frameworks Create tasks involving spatially variable gene identification and neighbourhood analysis Evaluate spatial relationships between cell populations and molecular features Design workflows combining imaging, expression, and spatial information where relevant Multi-Omic & Integrated Analysis Create problems involving integration of multiple molecular data types Develop workflows requiring alignment and interpretation across complementary genomic measurements Assess batch effects, biological variation, and integration quality Design tasks requiring careful selection of analytical approaches Evaluate whether computational conclusions are appropriately supported by the underlying data Topological & Advanced Data Analysis Develop specialised problems involving topological data analysis where relevant Apply tools such as GUDHI and related computational frameworks Create persistence-based analysis workflows Interpret topological structure within high-dimensional biological datasets Incorporate advanced quantitative methods where they provide meaningful biological insight Scientific Programming & Validation Write computational problem setups, oracle functions, and solution validators Use Python to build reproducible scientific workflows Verify numerical and biological correctness of expected outputs Identify software limitations, computational edge cases, and analytical failure modes Document assumptions, dependencies, parameters, and validation logic clearly Problem Testing & Refinement Test computational tasks against advanced systems Determine whether problems require genuine scientific reasoning rather than surface-level pattern matching Identify tasks that are too easy, ambiguous, or computationally impractical Refine prompts, constraints, datasets, and expected outputs to reach the intended difficulty Maintain strong standards of scientific accuracy and reproducibility Ideal Profile Master's degree, PhD, or equivalent research experience in Bioinformatics, Computational Biology, Genomics, Systems Biology , or a closely related STEM discipline Strong hands-on experience with single-cell genomics and computational biological analysis Proven proficiency with one or more specialised tools such as Scanpy, scVelo, Squidpy, GUDHI , or comparable scientific software Experience applying these tools to real research or professional projects Strong understanding of single-cell RNA-seq analysis, trajectory inference, spatial transcriptomics, or multi-omic integration Strong Python programming skills Ability to design rigorous computational problems and independently validate solutions Comfortable working in Linux and terminal-based environments Research publications, open-source contributions, or substantial professional computational biology work are highly valued Experience with scientific teaching, problem-set design, computational reproducibility, or containerised environments is advantageous Engagement Details Part-time independent contractor engagement Fully remote Expected commitment of at least 15–20 hours per week Flexible scheduling based on project requirements Compensation: $60–$90/hour Work involves computational problem design, reference-solution development, scientific validation, and iterative task refinement Projects may be extended, shortened, or concluded based on project needs and performance Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party H1-B and STEM OPT support is unavailable for this engagement About the Platform This opportunity is available through 24-MAG LLC. 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