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

About the position 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. Responsibilities • 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 • 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 • 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 • 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 • 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 • 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 • 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 • 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 Requirements • Graduate-level expertise in single-cell genomics • Expertise in scientific programming • Expertise in advanced computational analysis • 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 Nice-to-haves • 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 Benefits • 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