College Intern

Who We Are

Applied Materials is the global leader in materials science and engineering solutions that are at the foundation of virtually every new semiconductor chip and advanced display in the world. The equipment that we create and service is essential to advancing AI and accelerating the commercialization of next-generation semiconductor chips. Join us and push the boundaries of materials science and engineering in a company at the foundation of the electronics industry. The work we do together advances the world's technology.

What We Offer

Location:
Singapore,SGP

You'll benefit from a supportive work culture that encourages you to learn, develop, and grow your career as you take on challenges and drive innovative solutions for our customers. We empower our team to push the boundaries of what is possible-while learning every day in a supportive leading global company. Visit our Careers website to learn more.

At Applied Materials, we care about the health and wellbeing of our employees. We're committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our benefits.

• Learn how computational tools are used to investigate reaction pathways, molecular behaviour, and catalytic mechanisms.
• Benchmark ML modelling methods and DFT (Density Functional Theory) to analyse the energetics, structures, and reactivity of organic and organometallic molecules.
• Assist in building and running simulations, including evaluation of reaction intermediates, transition states, and microkinetic behaviour.
• Prepare input files, run calculations, analyse output data, and document results.
• Discuss modelling outcomes with chemists to help interpret mechanistic insights.

• Build, organise, and curate chemical datasets for modelling.
• Develop ML models (e.g., regression, classification, neural networks) to predict key physicochemical properties.
• Perform Python‑based data cleaning, feature engineering, model training, and evaluation.
• Work closely with chemists to translate modelling results into actionable design insights.
• Contribute to ML‑driven screening tools for predicting properties and identifying promising materials or molecules.

• Background in chemistry, chemical engineering, materials science, or related fields.
• Basic understanding of computational chemistry or quantum chemistry concepts.
• Experience with Python, data analysis libraries (NumPy, pandas, scikit‑learn), or ML frameworks is an advantage.
• Interest in combining theoretical chemistry with machine learning approaches.
• Good analytical thinking and attention to detail.