The purpose of this course is to broaden the PhD Fellow's knowledge beyond the main PhD dissertation topic. The PhD Fellow will, on his/her own accord, work through the curriculum with the intent of developing a broad and solid understanding of decision and data science as it applies to energy resources. The candidate will also do independent research in the topic area.
Upon completion of the course the PhD candidate should understand the fundamental principles of decision and data science as applied to energy resource engineering as well as understand how principles applies within research. The candidate should have a deep and broad understanding of how to apply these principles to support energy resources related decision making.
Skills needed to build a good basic decision model and to use it in generating powerful insights into the decision situation
Be able to apply and construct decision models and to use the most important elements in decision analysis relevant to engineering type decision-making in the face of uncertainty.
The PhD candidate should understand fundamental logical principles and analyses and be able to communicate their choices and recommendations clearly.
Required prerequisite knowledge
Form of assessment
Passed / Not Passed
• Achieve a grade of B or better in one of the courses MOD500, MOD550, PET685, or PET585
• Pass a project related to the course to be determined by the PhD student’s advisor
Achive a grad B or better in MOD500, MOD550, PET685 and PET585
There must be an early dialogue between the course coordinator, the student representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital course evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.