Master Thesis – Data-Efficient Exploration
Core
Develop active-learning strategies to reduce prediction uncertainty in first-principles calculations for cutting tool material compositions.
Role type
Master's thesis researcher (computational materials science)
Builds
Predictive models for mechanical properties of multicomponent compositions
Domain
Materials science / Computational chemistry / AI for science
Deliverable
research
Required skills
Python programming, scientific computing, data analysis, machine learning, active learning, first-principles calculations
Preferred skills
atomistic simulations, autoQMAS, density functional theory (DFT), computational materials science
Responsibilities
Conduct literature review, assess baseline models and uncertainty, develop and implement active-learning methods, perform final analysis and documentation, write thesis
Seniority
Master's student, full-time research project
