Computational Biology Internship Program

THE NEXORA GROUP • CAREER INTERNSHIP

Computational Biology with AI Internship

Build practical skills at the intersection of computational biology, bioinformatics, Python, data science, machine learning and Artificial Intelligence through a structured, project-focused internship.

• Computational Biology & Bioinformatics

• Python for Biological Data

• AI & Machine Learning Applications

• Practical Project Experience

• Internship Certificate

• Online & Remote Internship

Why Choose This Internship?

🧬 Biology + Computing

Explore modern computational approaches to biological problems.

🤖 AI Applications

Understand how AI and machine learning can support biological data analysis.

🐍 Python-Based Work

Develop practical programming and data-analysis capabilities.

📊 Project Portfolio

Build project evidence that can strengthen your academic and career profile.

📜 Internship Documentation

Eligible interns can receive applicable internship completion documentation.

56

Internship Modules

AI

Focused Learning

6

Duration Options

100%

Online & Remote

Ready to Start Your Computational Biology Journey?

Complete the registration form below to apply for the Computational Biology with AI Internship.

📌 Internship Start Date:

⏰ Last Date to Apply:

INTERNSHIP INTRODUCTION

Learn Computational Biology Through AI & Data

The Computational Biology with AI Internship by The Nexora Group is designed for students and early-career learners interested in combining biology with programming, data analysis, computational methods and Artificial Intelligence.

The internship introduces practical workflows used for biological data, sequence analysis, molecular data, machine learning and AI-assisted research workflows.

• Python and computational workflows

• Biological sequence and dataset analysis

• Machine learning for biological applications

• AI-assisted computational research

• Practical projects and portfolio development

WHAT YOU WILL EXPERIENCE

Build Skills Across Biology, AI & Computing

The internship combines computational foundations with biological datasets, AI concepts and practical project implementation.

🧬

Computational Biology

Understand computational approaches used to investigate biological systems and datasets.

🐍

Python Programming

Use Python for biological data processing, analysis and computational workflows.

🤖

AI & Machine Learning

Explore machine learning concepts and their potential applications in biological research.

📊

Biological Data Analysis

Work with structured datasets and develop analytical workflows.

🔬

Bioinformatics

Learn practical computational methods for sequence and molecular data.

📁

Project Portfolio

Organize your project work and documentation into useful career evidence.

COMPLETE INTERNSHIP CURRICULUM

56 Modules to Build Practical Expertise

A structured journey from computational biology fundamentals to AI-assisted biological data projects and portfolio development.

01

Introduction to Computational Biology

Understanding the field and modern applications.

02

Computational Biology Ecosystem

Tools, workflows and career pathways.

03

Biology Fundamentals

Core biological concepts for computational work.

04

Cell & Molecular Biology

Cells, molecules and biological systems.

05

DNA, RNA & Proteins

Understanding biological information.

06

Biological Databases

Introduction to major biological data resources.

07

Python for Computational Biology

Python foundations for biological applications.

08

Python Data Structures

Lists, dictionaries, tuples and biological data handling.

09

Functions & Modular Programming

Creating reusable computational workflows.

10

NumPy for Biological Data

Numerical computing and arrays.

11

Pandas for Bio Data

Dataframes, filtering and biological datasets.

12

Data Cleaning

Preparing biological datasets for analysis.

13

Data Visualization

Charts and visual exploration of biological data.

14

Statistical Foundations

Basic statistics for biological datasets.

15

Probability for Biology

Probability concepts for computational analysis.

16

Sequence Analysis

Working with biological sequences.

17

DNA Sequence Processing

Practical sequence manipulation.

18

RNA Sequence Analysis

RNA data and computational analysis.

19

Protein Sequence Analysis

Protein sequence exploration.

20

Sequence Alignment Concepts

Understanding biological sequence comparison.

21

BLAST Concepts

Understanding sequence similarity searching.

22

Genomic Data

Introduction to computational genomics.

23

Genomics Workflows

Building structured genomic analysis workflows.

24

Transcriptomics

Understanding gene-expression data.

25

Gene Expression Analysis

Working with expression datasets.

26

Biological Network Concepts

Networks and biological relationships.

27

Protein Structure Concepts

Computational view of protein structures.

28

Molecular Data

Understanding molecular datasets.

29

Introduction to Artificial Intelligence

AI concepts and biological applications.

30

Machine Learning Fundamentals

Core machine learning concepts.

31

Supervised Learning

Classification and regression workflows.

32

Unsupervised Learning

Clustering and pattern discovery.

33

Feature Engineering

Creating useful computational features.

34

Model Evaluation

Evaluating machine learning performance.

35

Biological ML Datasets

Preparing biological datasets for ML.

36

ML for Biological Classification

Building classification workflows.

37

AI-Assisted Data Analysis

Using AI tools to support analysis workflows.

38