FTL Programme Syria AI4Climate Implementation Machine Learning Training Support
Support the implementation of Machine Learning training for youth in Syria.
Overview
Support the implementation of Machine Learning training for youth in Syria.
You have:
- Senior-year undergraduate student or higher, preferably pursuing or holding a Bachelor's degree in Computer Science, Computer Engineering, or a related field, with a strong foundation in programming.
- Prior teaching, tutoring, mentoring, or instructional experience is a plus.
- Advanced proficiency in Python programming, Machine Learning and Deep Learning demonstrated with a project portfolio, work experience or certified training materials.
- Strong software development and problem-solving skills, with the ability to write clean, efficient, and maintainable code.
Contract
This is a UNV contract. More about UNV contracts.
UNDP ICPSD’s SDG AI Lab implements the Future Tech Leaders (FTL) Programme to equip the next generation with cutting-edge digital skills. The Programme focuses on frontier technologies such as AI, machine learning, data science, GIS, and game development, while also strengthening entrepreneurship and leadership capacities. It primarily targets the labor force in developing countries, with a particular emphasis on youth.
Frontier technologies present leapfrogging opportunities in fragile contexts like Syria. The Future Tech Leaders Programme equips young people with frontier tech skills, builds local capacity, and supports pilot projects to reduce risks for partners and investors. By linking training to urgent climate challenges, Syrian youth can create jobs, strengthen resilience, and open future-oriented employment and innovation pathways in a country facing acute unemployment.
The Online Volunteers will provide support on the areas mentioned below:
- Review the existing Machine Learning course materials and update the course materials, exercises and assignments in accordance with the Climate Resilience and latest best practices.
- Deliver "Machine Learning Specialization" courses to ensure a comprehensive understanding among students in the absence of FTL Trainers (if necessary).
- Demonstrate practical skills through live coding sessions.
- Provide a pre-recorded demo for each session, if needed.
- Host office hours for addressing students' questions and assisting them for their assignments, teaching, and interaction with students.
The Office hours and workload will be discussed with the Online Volunteers.
- Senior-year undergraduate student or higher, preferably pursuing or holding a Bachelor's degree in Computer Science, Computer Engineering, or a related field, with a strong foundation in programming.
- Prior teaching, tutoring, mentoring, or instructional experience is a plus.
- Advanced proficiency in Python programming, Machine Learning and Deep Learning demonstrated with a project portfolio, work experience or certified training materials.
- Strong software development and problem-solving skills, with the ability to write clean, efficient, and maintainable code.
Potential interview questions
| Can you give an example of a machine learning project you've worked on and how you handled challenges during development? | This question assesses your practical experience and problem-solving skills in machine learning. | Describe your project, the challenges faced, and how you overcame them. |
| How would you explain the concept of deep learning to a beginner? | The interviewer wants to evaluate your ability to teach complex concepts simply and effectively. | Pro members can see the explanation. |
| What methods do you use to keep your programming skills updated? | Pro members can see the explanation. | Pro members can see the explanation. |
| Describe a time when you had to adapt your teaching style to accommodate different learning abilities in your students. | Pro members can see the explanation. | Pro members can see the explanation. |
| What tools do you prefer for assessing student performance in a machine learning course? | Pro members can see the explanation. | Pro members can see the explanation. |
| How do you approach preparing a course syllabus and material for a machine learning class? | Pro members can see the explanation. | Pro members can see the explanation. |
| What strategies do you use to engage students during online classes? | Pro members can see the explanation. | Pro members can see the explanation. |
| How would you handle a situation where a student is struggling with a particular concept in machine learning? | Pro members can see the explanation. | Pro members can see the explanation. |
Added 10 hours ago
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Updated 48 minutes ago
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Source:
unv.org
