Interdisciplinary AI Opportunities at Albarqouni Lab

Bachelor and Master Theses (6-9 months)

• Stay tuned or simply approach me for a BA/MA thesis

Short-term Visits (2-12 weeks)

• Stay tuned – follow us on LinkedIn for Up-to-Date news

Research Assistant Positions (3-6 months – Remote)

• Research Assisant (AI Model for polyphenol quantification in pomegranates)

  Deliverables	Description
  #1	Developing an AI-driven model for real-time polyphenol quantification in pomegranates. This involves leveraging machine learning algorithms and AI techniques to analyze the dataset obtained from the image analysis
  #2	Optimization of the AI algorithm to improve the accuracy and efficiency of polyphenol prediction. This may include feature selection, data preprocessing, and model fine-tuning
  #3	Cross-validation of the AI model to ensure its robustness and reliability in predicting polyphenol content in different pomegranate samples
  #4	Integration of the AI model in a user-friendly mobile application. The application should allow consumers to quantify polyphenols in pomegranates through simple photography and display nutritional information

PhD/Research Positions (3 years)

• Ph.D. position in AI in Medicine - Robustness and Explainability (m/f/d)

Summer School on AI (5 days)

• Stay tuned – follow us on LinkedIn for Up-to-Date news

AI Beginner’s Roadmap

Phase 1: Foundations

Learn Python

• Familiarize yourself with Python basics.

Understand Mathematics

• Brush up on fundamental math concepts using resources like Khan Academy.

Learn about Data Science

• Explore data manipulation with NumPy and Pandas.
• Visualize data using Matplotlib.

Phase 2: Basic AI Concepts

Introduction to Machine Learning

• Get started with the free Machine Learning Crash Course by Google. You probably need to consider this ML course from Andrew Ng.

Dive into Deep Learning

• Explore neural networks with free resources on Google Colab. You may consider this DL course from Stanford University.
• Try hands-on projects on Kaggle.

Phase 3: Specialize in AI Subfields

Natural Language Processing (NLP)

Computer Vision

Generative AI

Phase 4: Advanced Topics

Advanced Deep Learning

Model Deployment

Stay Updated

• Follow research papers on arXiv.
• Stay engaged with the AI community on free platforms like Twitter.

Phase 5: Build a Portfolio

Create a GitHub Portfolio and Contribute to Open Source

• Showcase your projects and code on GitHub, utilizing free private repositories.

Networking

• Join free AI communities on LinkedIn and Twitter.
• Participate in free virtual meetups and conferences.

Phase 6: Continuous Learning

Pursue Further Education

• Explore free advanced courses on platforms like Coursera (audit mode) and edX.

Specialize Based on Interests

• Delve deeper into specific areas based on your interests.

Internship and Research Visits