We are sharing a specialised part-time consulting opportunity for PhD-level biologists with strong scientific computing expertise across genetics, biochemistry, ecology, and computational research.
This role focuses on designing original, executable research problems grounded in authentic biological workflows. Selected experts will develop challenging coding-based tasks, create rigorous reference solutions and grading criteria, and test and refine problems until they require genuine research-level biological and computational reasoning.
Key Responsibilities
Computational Biology Problem Design
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Develop original research-level computational biology problems
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Build tasks from published papers, public datasets, open-source repositories, or independently designed scientific scenarios
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Create problems requiring multi-step biological, statistical, and computational reasoning
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Ensure tasks reflect realistic research workflows rather than standard textbook exercises
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Design problems with scientifically defensible and reproducible solutions
Genetics & Genomic Analysis
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Develop computational tasks involving genetics and related biological datasets
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Create problems requiring interpretation of inheritance, variation, population, or molecular genetic information
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Incorporate realistic biological assumptions, experimental constraints, and data limitations
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Design tasks requiring quantitative and computational investigation
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Apply advanced genetics expertise to task validation and difficulty calibration
Biochemistry & Molecular Biology
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Create problems involving biochemical pathways, molecular interactions, kinetics, or related quantitative biology
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Develop workflows requiring analysis of experimental or simulated biochemical data
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Evaluate assumptions, parameters, and biological interpretation
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Incorporate realistic experimental limitations and edge cases
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Ensure problems require substantive biochemical reasoning rather than surface-level recall
Ecology & Quantitative Biology
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Develop computational tasks involving ecological systems, populations, communities, or environmental datasets
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Design problems requiring statistical modelling, simulation, or interpretation of complex biological relationships
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Evaluate assumptions involving sampling, variability, environmental effects, and biological interactions
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Create scenarios that require careful interpretation of noisy or incomplete data
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Apply quantitative reasoning to realistic ecological research questions
Scientific Computing & Programming
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Write and validate scientific workflows using Python, R, or another relevant programming language
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Develop computational setups, reference calculations, and solution validators
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Debug scientific code and identify implementation or numerical issues
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Build reproducible workflows suitable for automated testing
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Ensure computational outputs accurately reflect the underlying biological problem
Research Task Development
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Source appropriate scientific material from research papers, datasets, repositories, or original scenarios
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Translate complex biological research into clearly specified computational tasks
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Define inputs, assumptions, constraints, and expected outputs
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Create assignments requiring both biology expertise and coding proficiency
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Ensure task difficulty comes from scientific reasoning rather than unnecessary complexity
Reference Solutions & Grading Criteria
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Produce authoritative reference solutions and supporting computational analyses
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Define clear criteria describing what constitutes a correct solution
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Identify essential biological reasoning, computational steps, and expected outputs
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Develop grading logic capable of distinguishing correct solutions from plausible but flawed approaches
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Ensure evaluation criteria are precise, consistent, and reproducible
Testing & Difficulty Calibration
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Test tasks against advanced computational systems
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Analyse scientific, computational, and reasoning failure modes
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Refine prompts, inputs, constraints, and expected outputs based on testing
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Adjust task difficulty while preserving scientific validity
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Finalise tasks only when they reliably require advanced biological and computational expertise
Research Engineering Workflow
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Work through a Git/GitHub pull-request workflow
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Run and validate code within Docker-based environments
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Respond to automated quality checks and reviewer feedback
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Maintain clean, reproducible code and supporting documentation
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Collaborate effectively within structured scientific software workflows
Ideal Profile
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PhD required in Biology, Biological Sciences, Biochemistry, Genetics, Ecology, or a closely related field
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Demonstrated expertise in at least two of the following:
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Genetics
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Biochemistry
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Ecology
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Strong working proficiency in Python, R, or another scientific programming language
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Hands-on experience using code for biological research, modelling, simulation, or data analysis
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Comfortable with Git/GitHub
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Experience running code in Docker or other containerised environments
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Strong understanding of reproducible scientific computing
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Ability to translate advanced biological research into clearly defined computational problems
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Peer-reviewed publications are highly valued
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Prior scientific software or research engineering experience is advantageous
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Strong written communication and ability to document biological assumptions, methods, and solutions precisely
Engagement Details
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Part-time independent contractor engagement
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Fully remote
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20+ hours per week
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Initial duration of approximately 6 weeks
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Immediate start
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Compensation: Up to $60/hour
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Work includes computational biology problem design, scientific coding, reference-solution development, grading criteria, testing, and task refinement
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Projects may be extended, shortened, or concluded based on project needs and performance
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Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party
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H1-B and STEM OPT support is unavailable for this engagement
About the Platform
This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.
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