Physics PhD Fellow Job at Bengaluru, Karnataka, India Full Time NEW

Capacity is building a next-generation physics learning platform for undergraduate, postgraduate, and research-level students. Our goal is to make world-class physics learning more accessible by combining deep pedagogy, high-quality content, and AI-powered tutoring support.

We are starting with physics because it is one of the hardest subjects to learn deeply. Students often do not just need answers. They need to understand where their reasoning breaks, why a derivation fails, and how to build intuition step by step.

Capacity is designed to help students debug their understanding, improve their derivation skills, and learn physics with the kind of support they would ideally get from an exceptional one-on-one tutor.

About the Role

We are looking for a Physics PhD Fellow to work closely with the Capacity team as a pedagogy expert, academic reviewer, and product-building partner.

This role is for someone who is currently pursuing a PhD in Physics and deeply cares about how students learn physics. You will help us design learning experiences, review physics explanations, improve AI tutor behavior, create high-quality academic workflows, and ensure that Capacity teaches physics in a rigorous, intuitive, and student-friendly way.

You will not be a traditional campus ambassador. This is a much deeper role. You will help shape the academic quality and learning philosophy of the product.

Who We Are Looking For

You should be currently pursuing a PhD in Physics and should have strong comfort with undergraduate and postgraduate physics topics.

We are especially interested in candidates who enjoy teaching, mentoring, tutoring, TA work, explaining difficult concepts, or helping students debug derivations and problem-solving mistakes.

Key Responsibilities1. Physics Pedagogy and Learning Design

Help us design how Capacity should teach physics concepts, derivations, and problem-solving methods.

You will help answer questions like:

• What is the right way to explain this concept to an undergraduate student?
• Where do students commonly get stuck?
• What are the most frequent mistakes in derivations?
• How should a student be guided without simply giving away the final answer?
• How do we move from formula memorization to real conceptual understanding?

2. Derivation and Problem-Solving Review

Review physics derivations, solutions, explanations, and AI-generated responses for correctness and clarity.

You will help ensure that Capacity can:

• Identify the first wrong step in a derivation
• Explain why a step is wrong
• Guide the student toward the right reasoning
• Avoid hallucinated or misleading physics explanations
• Maintain academic rigor while still being student-friendly

3. AI Tutor Quality Improvement

Work with the product and AI team to improve how the Capacity tutor responds to student questions.

You will help evaluate:

• Whether the AI explanation is correct
• Whether it is pedagogically useful
• Whether it gives away too much too quickly
• Whether it encourages real learning
• Whether it adapts to UG and PG student levels

4. Curriculum and Content Support

Help us build and review high-quality learning content across core physics areas such as:

• Classical Mechanics
• Electromagnetism
• Quantum Mechanics
• Statistical Mechanics
• Mathematical Methods
• Thermodynamics
• Condensed Matter
• Optics
• Relativity
• Research-level or advanced physics topics, depending on your expertise

5. Student Learning Insights

Bring your experience as a PhD student, TA, tutor, or mentor to help us understand real student pain points.

You may help us identify:

• Common misconceptions
• Difficult derivation patterns
• High-friction topics
• Exam and qualifier prep needs
• Differences between UG, PG, and research-level learners

6. Product Feedback

Use Capacity as an expert user and give structured feedback to improve the product.

You will help us think through:

• What feels useful?
• What feels confusing?
• Where does the product break?
• Where can the AI tutor become more accurate?
• What would make students come back every week?

RequirementsMust Have

• Currently pursuing a PhD in Physics
• Strong foundation in undergraduate and postgraduate physics
• Ability to explain complex physics concepts clearly
• Comfort reviewing derivations and problem-solving steps
• Strong written communication skills
• Genuine interest in physics education and student learning
• Ability to give precise, structured feedback
• High academic integrity and attention to correctness

Good to Have

• Prior experience as a Teaching Assistant, tutor, mentor, or lecturer
• Experience helping students prepare for exams or qualifying exams
• Familiarity with LaTeX
• Interest in AI, edtech, or learning tools
• Experience creating physics content, notes, videos, or problem sets
• Familiarity with common physics textbooks such as Griffiths, Goldstein, Jackson, Sakurai, Shankar, Landau, or similar

Ideal Candidate

You may be a great fit if you:

• Love physics and enjoy explaining it
• Often help classmates or juniors understand difficult concepts
• Care about both mathematical rigor and intuitive understanding
• Can spot subtle mistakes in derivations
• Believe students should learn reasoning, not just final answers
• Are excited about using AI to improve education
• Want to help build a product used by physics students around the world

What You Will Work On

Depending on your strengths, you may work on:

• Reviewing AI tutor responses
• Creating derivation-debugging examples
• Designing learning flows for hard physics topics
• Building qualifier-style practice workflows
• Auditing explanations for correctness
• Helping define what “good physics tutoring” means inside Capacity
• Testing the product with real student problems
• Advising the team on academic quality and pedagogy

Time Commitment

This is a flexible fellowship role designed to work alongside your PhD.

Expected commitment: 5 to 10 hours per week, depending on availability and project needs.

Compensation

This can be structured as a paid fellowship, stipend-based role, or part-time consulting role depending on experience, contribution level, and time commitment.

High-performing fellows may also be considered for longer-term academic, product, or advisory roles with Capacity.

Why Join Capacity

• Help build a physics learning platform from the ground up
• Work on a product designed for serious UG, PG, and research-level learners
• Shape how AI can teach physics responsibly
• Bring your academic expertise into a real product used by students
• Collaborate directly with the founding team
• Build a strong public and professional profile at the intersection of physics, pedagogy, and AI

How to Apply

Please share:

• Your current university and PhD program
• Your area of research
• Any teaching, TA, tutoring, or mentoring experience
• A short note on why you are interested in physics education
• One example of a physics topic you think students commonly misunderstand, and how you would explain it better

Role Title

Physics PhD Fellow

Role Type

Part-time / Remote / Fellowship

Function

Physics Pedagogy, Academic Quality, AI Tutor Evaluation, Learning Design