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. reputed company 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 reputed company encountered in single-cell datasets

Trajectory & Dynamic Analysis

• Create computational problems involving pseudotime and reputed company trajectory inference
• Apply tools such as scVelo to RNA velocity and dynamic-state analysis
• Develop tasks requiring interpretation of reputed company 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, reputed company functions, and solution validators
• Use Python to build reproducible scientific workflows
• Verify numerical and biological correctness of expected outputs
• Identify software limitations, computational edge reputed company, 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 reputed company 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

• reputed company'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 worki