Program Learning Outcomes - Modelling and Computational Science, PhD

By the end of the program, students graduating will be able to:

• Review the existing literature related to a specific scientific subject.
• Analyze a practical problem and choose (or formulate) a suitable model to describe it (e.g., continuous/discrete, deterministic/stochastic, etc.).
• Identify (or develop) appropriate simulation approaches, algorithms, numerical schemes, and analytic methods for solving a problem at hand.
• Implement codes (using existing packages when appropriate) within practical scientific computational environments.
• Evaluate and interpret computed results critically.
• Communicate scientific ideas, concepts and results effectively in writing and verbally.
• Transfer modelling and computational skills to a variety of domain-specific contexts.
• Develop predictive models based on structured and unstructured data.
• Effectively visualize output and analysis from computational models.
• Formulate specific research questions relevant to challenges in modelling and computational science.
• Demonstrate critical awareness of the relevant scientific literature, current problems and new knowledge about a particular area of research within modelling and computational science.