About the role
Build practical skills at the intersection of genomics, computational biology, bioinformatics, artificial intelligence and data-driven life sciences through a structured project-based internship.
Practical Genomics & Bioinformatics Projects
AI Applications in Genomics & Life Sciences
Python, Data Analysis & Computational Workflows
Online & Remote Internship
Internship Certificate & Applicable Documentation
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.
56 Internship Modules
AI Focused Learning
6 Duration Options
100% Online & Remote
About the Internship
Learn Genomics + AI Through Practical Work
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
More Than Just an Internship
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
56 Modules of Genomics with AI
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.
Machine Learning Fundamentals
Core concepts of supervised and unsupervised learning.
AI for Biological Data
Applying AI concepts to life-science datasets.
Feature Engineering
Preparing useful computational features from biological data.
Classification Models
Understanding classification approaches for biological data.
Regression Concepts
Regression approaches and biological applications.
Clustering Biological Data
Unsupervised learning and biological grouping.
Model Evaluation
Evaluating machine learning model performance.
AI-Assisted Genomic Analysis
Using AI tools to support genomic workflows.
Gen