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Guided learning journeys that build knowledge step by step.
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A comprehensive learning path for computational physics practitioners to understand and apply ethical principles in research. It covers scientific integrity, reproducibility, data management, dual-use concerns, and responsible conduct, culminating in an ethical decision-making framework.
This learning path equips physics researchers with essential project management skills tailored to computational physics. It covers research planning, resource management, team collaboration, milestone tracking, and software management, integrating these with research experience to ensure practical applicability.
This learning path provides a structured journey from the fundamental principles of quantum mechanics to advanced computational methods used in quantum chemistry. It covers electronic structure theories, molecular properties, reaction pathways, and spectroscopy, emphasizing practical applications for chemistry and physics students. The path is designed for professionals seeking to develop career skills in computational chemistry.
A structured learning path for physics researchers to master GPU computing, covering hardware architecture, parallel programming (CUDA/OpenCL), performance optimization, and practical physics applications. Designed for professionals with HPC programming experience.
A comprehensive learning path for physics researchers to master major computational physics software packages (FEniCS, deal.II, COMSOL, LAMMPS, Abinit) and integrate them into professional research workflows. It covers programming fundamentals, numerical methods, software-specific usage, and advanced scripting and workflow automation.
This graduate-level learning path equips physics and energy students with the computational skills needed to model, simulate, and analyze energy systems—from solar and fusion to fission, storage, and thermoelectrics. It bridges core physics and thermodynamics with numerical methods, covering both foundational techniques and domain-specific applications. The curriculum progresses from essential mathematics and programming to advanced simulation and optimization, culminating in practical projects for each energy technology.
This graduate-level path equips medical physics students with the computational skills needed for imaging, dosimetry, treatment planning, and biological modeling. It bridges fundamental physics with numerical methods and their clinical applications.
This learning path guides climate science students through the essential physical principles and computational techniques needed to understand and build climate models. It covers atmospheric physics, radiative transfer, ocean dynamics, feedbacks, and the numerical methods that tie them together.
A systematic graduate-level path to master computational modeling of photonic structures. It covers the essential physics of light in periodic and guided media, the core numerical methods (FDTD, eigenmode expansion, plane-wave expansion), and practical simulation and design skills for photonic crystals, waveguides, and nanophotonic devices.
This advanced graduate path equips physics students with the theoretical foundations and computational techniques to model soft matter systems such as polymers, colloids, liquid crystals, and self-assembling structures. Starting from statistical mechanics and programming essentials, it progresses through simulation methods and specific soft matter applications, culminating in a capstone project.