Who should apply?
Curious students and early-career applicants who want to use computational methods on real scientific problems. Previous research experience is welcome, but not essential.
Computational Pipelines & Agentic AI for Scientific Research
We develop computational pipelines, agentic AI workflows, and no-code scientific tools for research organizations, research institutes, and life science teams working with complex biological and computational data.
From biological data analysis and reproducible computational workflows to specialized research agents that coordinate analysis, evidence, tools, and validation, DataLens.Tools helps research teams build structured computational systems around their scientific questions.
Get in TouchApplications Open
DataLens.Tools Student Talent Programme 2026
An international programme for students and early-career researchers interested in computational biology, scientific Python, data science and responsible AI-assisted research.
We are interested not only in what applicants already know, but in how they reason, learn and approach unfamiliar scientific problems.
Apply NowWhat we are looking for
Scientific curiosity, a willingness to learn and thoughtful problem-solving. Basic Python is helpful, and motivated applicants who are actively learning are encouraged to apply.
Think scientifically
Understand unfamiliar research problems, evidence and experimental design.
Work computationally
Apply Python, data analysis and reproducible research practices.
Use AI critically
Use LLMs productively while retaining scientific judgement.
Areas may include bioinformatics, cheminformatics, single-cell analysis, scientific Python, data science, AI-assisted research and reproducible workflows. Selected participants receive structured training; strong participants may later be considered for paid projects, internships or Working Student positions, subject to project availability, skills and eligibility.
New Launch
Biological Tabular Data Advisor
A scientific workflow advisor that helps biology researchers decide which statistical or machine learning approach fits their current experimental dataset.
🧬
Not just AutoML, biology-aware guidance
Built for experimental biology, not generic business tables.
Upload a CSV file and the advisor inspects your dataset structure, detects biological patterns, and recommends a scientifically defensible analysis strategy. It is especially useful for fluorescence data, cell-line assays, imaging-derived features, dose-response experiments, and longitudinal measurements.
Detects structure
Time-series, wide-format tables, repeated measures, batch/plate columns, and measurement readouts.
Prevents bad ML
Flags leakage risks, inappropriate random splitting, small-sample overfitting, and temporal autocorrelation.
Recommends workflows
Suggests preprocessing, validation, visualization, mixed-effects models, and time-series analysis when needed.
Exports reports
Generate Markdown or JSON summaries for notebooks, lab records, documentation, or project planning.
Computational Research Agents
Biology-aware AI agents for research workflows
DataLens.Tools designs computational agents for biology, bioinformatics, neuroscience, chemical ecology, and data-intensive research. These systems can coordinate scientific tasks, inspect structured data, use computational tools, organize evidence, and support reproducible analytical workflows.
We do not treat a research agent as a generic chatbot. Each system is designed around the scientific question, biological data structure, computational environment, validation requirements, and human review points of the organization using it.
Scientific workflow coordination
Break complex computational research tasks into inspectable, specialist steps.
Data & metadata auditing
Check experimental structure, repeated measures, leakage risks, and analysis readiness.
Tool-enabled analysis
Connect approved scripts, databases, statistical tools, and computational pipelines.
Human-reviewed decisions
Keep uncertainty, evidence, and approval points visible to researchers.
Conceptual communication visualization. Client systems are developed around specific research workflows, data structures, validation requirements, tool access, and institutional safeguards.
Interactive advisor
Find a sensible first ML model for your biological dataset
Model Match asks a few questions about your samples, target, feature space, biological design, preprocessing, and validation plan. It returns a practical model shortlist with risks to check before trusting the result.
Step 1
Describe the dataset
Step 2
Review model fit
Step 3
Check analysis risks
Expert Review
Biological ML Validation Audit
Is your model performing well or is your validation strategy making it look better than it really is?
DataLens.Tools reviews biological machine-learning workflows for experimental-design, validation, and data-leakage problems that can produce misleading performance estimates.
The audit is intended for biological datasets where small sample sizes, repeated measurements, batches, patients, experimental groups, or high-dimensional features can affect model selection and evaluation.
Experimental structure
Review biological units, repeated measurements, batches, groups, and independence assumptions.
Leakage & splitting
Examine train/test structure, preprocessing leakage, feature-selection leakage, and grouped validation.
Model evaluation
Review baselines, metrics, hyperparameter tuning, overfitting risks, and validation design.
Scientific defensibility
Assess whether evaluation and reported performance support the intended biological claim and generalization setting.
📘 New Digital Book
Python & AI for Modern Biological Research
A practical guide for biologists to learn Python, data analysis, statistics, machine learning, imaging workflows, and AI for modern research.
Designed for students, researchers, and scientists who want a clear, hands-on path into computational biology without getting lost in overly technical material.
Python for biological data
Statistics made intuitive
Machine learning for biology
Imaging and time-series workflows
Instant access after payment. Secure checkout powered by Stripe.
Personalized Data Analysis Services
Need help cleaning, analyzing, or visualizing your research data? Book a 1-on-1 consultation tailored to your project in biology, neuroscience, or imaging.
Learn More & Book NowFeatured Tools
New Launch
Biological Tabular Data Advisor
Get biology-aware recommendations for ML/statistical workflows, validation, preprocessing, and visualization from your CSV dataset.
Open Tool →Fluro Motion Corrector
Correct motion artifacts in fluorescence recordings and improve ΔF/F accuracy before analysis.
Learn More →FLIR IR Image Analyzer
Analyze and interpret thermal images from FLIR cameras with region statistics and automated reports.
Learn More →Neuroscience Tools (New!)
CalciumTrace: ΔF/F Extractor
Extract and visualize ΔF/F calcium imaging signals with automated baselining and export options.
Learn More →SpikeTrain Visualizer
Generate raster plots and PSTHs from spike trains — no coding needed, just upload your data.
Learn More →TrackViz: Behavior Heatmaps
Visualize movement paths, heatmaps, and velocity maps from behavioral tracking data in seconds.
Learn More →Learning Hub
Step-by-step tutorials for modern biology, bioinformatics, and data analysis workflows. Learn practical methods you can apply directly to your own datasets.
RNA-seq
Build Gene Regulatory Networks from RNA-seq Using GENIE3
Learn how to infer gene regulatory relationships from RNA-seq data using GENIE3 and translate expression data into biological network insight.
Open Tutorial →
Single-Cell
Single-Cell RNA-seq Quality Control & Cell Filtering
A practical guide to filtering low-quality cells, assessing QC metrics, and preparing single-cell RNA-seq data for downstream analysis.
Open Tutorial →Contact Us
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