Genomics Internship

Build practical skills at the intersection of genomics, computational biology, bioinformatics, artificial intelligence and data-driven life sciences through a structured project-based internship.

Why Genomics with AI?

• Genomics-Focused Learning: Understand genomic data, sequencing concepts and computational analysis.
• AI & Machine Learning: Explore AI-assisted approaches for biological and genomic datasets.
• Practical Projects: Work through structured projects designed around real-world workflows.
• Career Portfolio: Create project evidence that can support your academic and career profile.

About the Internship

The Genomics with AI Internship by The Nexora Group is designed for students and early-career learners interested in genomics, computational biology, bioinformatics, biotechnology and artificial intelligence.

The internship combines foundational concepts with structured computational workflows, genomic data analysis, Python programming, machine learning and AI-assisted approaches to biological data.

• Genomics & biological data fundamentals
• Python for computational biology
• Genomic data processing & visualization
• Machine learning concepts for biological datasets
• AI-assisted genomics workflows
• Project documentation & portfolio development

What You Can Explore

• Genome organization and sequencing concepts
• DNA and RNA sequence analysis
• Biological databases
• Python and computational workflows
• Genomic data preprocessing
• Variant analysis concepts
• Gene expression analysis
• Machine learning fundamentals
• AI-assisted biological data interpretation
• Genomics project development

Why Join

Build knowledge, practical project experience and career-ready evidence around one of the rapidly developing areas of computational life sciences.

• Genomics Fundamentals: Understand core genomics concepts, sequencing, genomic datasets and computational approaches.
• AI for Life Sciences: Explore machine learning and AI-assisted workflows for biological and genomic data.
• Python Skills: Work with Python-based approaches for biological data processing and analysis.
• Data Analysis: Learn practical approaches for preparing, analyzing and visualizing biological datasets.
• Project Experience: Complete structured projects that can be documented as part of your portfolio.
• Internship Documentation: Eligible interns may receive applicable internship completion documentation based on program requirements.

Complete Internship Curriculum

A structured curriculum progressing from genomics fundamentals and Python to AI-assisted analysis, projects and portfolio development.

• Introduction to Genomics: Genomics concepts, applications and career pathways.
• Genomic Data Ecosystem: Understanding modern genomic data resources.
• DNA Structure & Organization: DNA structure, chromosomes and genome organization.
• RNA & Gene Expression: RNA biology and gene expression fundamentals.
• Sequencing Technologies: Introduction to sequencing technologies and workflows.
• NGS Fundamentals: Next-generation sequencing concepts.
• Genomic File Formats: FASTA, FASTQ, SAM, BAM, VCF and related formats.
• Biological Databases: Exploring major biological data resources.
• NCBI & Sequence Resources: Searching and retrieving biological information.
• Genomic Data Retrieval: Collecting datasets for computational analysis.
• Python Fundamentals: Python syntax, variables and programming concepts.
• Python Data Structures: Lists, dictionaries, tuples, sets and data handling.
• Python for Genomics: Applying Python to biological sequence data.
• Biopython Fundamentals: Working with biological sequences programmatically.
• Sequence Manipulation: Practical DNA and RNA sequence operations.
• Sequence Quality Concepts: Understanding sequence quality and preprocessing.
• Sequence Alignment: Fundamentals of sequence alignment.
• BLAST Concepts: Sequence similarity searching and interpretation.
• Genomic Data Cleaning: Preparing biological datasets for analysis.
• Data Preprocessing: Transforming genomic data into analysis-ready datasets.
• Genomic Data Visualization: Creating meaningful biological data visualizations.
• Statistical Concepts: Statistics for biological and genomic datasets.
• Gene Expression Data: Introduction to expression datasets and analysis.
• Expression Analysis Workflow: Structured gene-expression analysis concepts.
• Variant Analysis Concepts: Understanding genomic variants and their interpretation.
• Variant Data Formats: Working with variant-oriented datasets.
• Genome Annotation: Introduction to annotation and biological interpretation.
• Functional Genomics: Connecting genomic information with biological function.
• Introduction to AI: Artificial intelligence concepts and applications.