Artificial Intelligences for Science Research

Enroll Now!

  • Level
  • Duration
  • Certificate
  • All Levels
  • 6 weeks (12 sessions, 1.5 hours per session)
  • Bharat Academy Education
  • Certified
  • - KHDA
    - AIAL
    - Royal Institute for Chartered Engineer - RICE (USA)
Course Info
Key Highlights
Target Audience
Course Overview

This course aims to provide researchers with the fundamental knowledge and skills needed to leverage Artificial Intelligence (AI) in scientific research. Participants will explore various AI techniques, learn how to apply them in different scientific domains, and understand their transformative role in accelerating discovery and innovation.

Learning Objectives
  • Understand the role of AI in modern scientific research
  • Learn key AI methods such as machine learning, deep learning, and natural language processing (NLP)
  • Apply AI models to solve problems in various scientific fields (e.g., biology, physics, chemistry)
  • Interpret AI models for predictive analysis and decision-making in research
  • Explore case studies of AI in science, including drug discovery, genomics, climate modeling, and more
  • Gain hands-on experience in using AI tools and frameworks in scientific research
Assessment & Certification
  • Assignments: Weekly practical assignments to reinforce AI concepts and techniques
  • Project: Final capstone project to demonstrate the application of AI to a real-world scientific research problem
  • Quizzes: Regular quizzes to assess theoretical knowledge
  • Certification: Certificate of completion for participants who successfully complete all course modules
Prerequisites
  • Basic understanding of programming (Python preferred)
  • Familiarity with data science concepts (optional but beneficial)
Module 1: Introduction to AI in Scientific Research
  • Introduction to AI: Overview of AI and its historical development
  • AI in Science: The impact of AI in accelerating scientific discovery
  • Case Studies: Notable AI-driven breakthroughs in research (e.g., AlphaFold in protein folding)
Module 2: Fundamentals of Machine Learning
  • Types of Machine Learning: Supervised, unsupervised, and reinforcement learning
  • Data for Scientific Research: Preparing scientific datasets for machine learning
  • Key Algorithms: Decision trees, support vector machines, and neural networks
  • Applications: How machine learning is applied in fields like genomics, environmental science, and material science
Module 3: Deep Learning for Complex Problems
  • Introduction to Deep Learning: Neural networks, CNNs, RNNs, and LSTMs
  • Deep Learning Architectures: Understanding model architectures in scientific problems
  • Use Cases in Science: Applications in image analysis (e.g., medical imaging), signal processing (e.g., radio astronomy), and molecular modeling
  • Practical Session: Hands-on deep learning model building using frameworks such as TensorFlow or PyTorch
Module 4: Natural Language Processing (NLP) in Research
  • Introduction to NLP: How machines understand and process human language
  • Text Mining in Research: Extracting knowledge from research papers, datasets, and scientific articles
  • AI-Assisted Literature Reviews: Tools like GPT and BERT for automated data extraction and analysis
  • Practical Session: Using NLP to automate the extraction of key information from scientific literature
Module 5: AI for Predictive Analytics in Scientific Experiments
  • Predictive Modeling: How AI can be used to predict outcomes in experiments and simulations
  • Uncertainty in AI Models: Managing and interpreting uncertainty in scientific AI models
  • Practical Applications: Predictive models for climate science, physics simulations, and biological processes
Module 6: AI for Hypothesis Generation and Experimentation
  • Automated Science: AI-driven hypothesis generation, experimental design, and result analysis
  • Robotic Labs: How AI powers robotic platforms for automated experiments in chemistry and biology
  • AI in Drug Discovery: Designing AI systems for drug discovery and protein analysis
Module 7: Ethics and Responsible AI in Scientific Research
  • Ethical Considerations: Responsible use of AI in research, addressing bias, fairness, and reproducibility
  • Data Privacy: Protecting sensitive research data with AI
  • AI and Research Integrity: Ensuring transparency and reproducibility in AI-driven research
Module 8: AI Tools and Frameworks for Scientific Research
  • AI Software Tools: Introduction to popular AI platforms (e.g., TensorFlow, Keras, Scikit-learn)
  • Data Science Environments: Using Jupyter notebooks, cloud-based AI services, and GPU computing
  • Collaborative Tools: Open-source AI research platforms, GitHub, and cloud computing resources for researchers
Module 9: Case Studies of AI in Different Scientific Disciplines
  • AI in Biology: AI in genomics, protein structure prediction, and molecular biology
  • AI in Physics: AI for particle physics, quantum simulations, and cosmology
  • AI in Chemistry: Drug discovery, material science, and reaction prediction
  • AI in Earth Sciences: Climate modeling, geosciences, and environmental prediction
Module 10: Capstone Project: AI for Solving a Real Research Problem
  • Project Design: Formulate a research problem where AI can provide a solution
  • Implementation: Apply AI techniques learned in the course to a scientific dataset or problem
  • Presentation and Peer Review: Present findings and AI model results, receive feedback from peers and instructors
Target Audience
  • Researchers and scientists from any domain interested in learning AI
  • Graduate and PhD students in scientific fields
  • Industry professionals involved in R&D

Courses

  • Artificial Intelligence
  • Robotics
  • Healthcare Services
  • Information Technology
  • Accounts and Legal
  • Cyber Security | Ethical Hacking
  • Engineering
  • Finance
  • Hotel and Hospitals
  • Satellite Communication
  • Fire and Safety
  • JCI
  • Amadeus GDS

Contact

Working Hours

Saturday – Thursday : 9:00 am – 8:00 pm

Office Phone Number

(+971) 54 206 1051

Bharat Academy Email

marketing@bharat-academy.com

Campus Building

5007, Rigga Business Center, Ibis Hotel, Al Rigga Metro, Dubai

Newsletter

Payment Modes

© 2024 Bharat Academy Education

Play Video