PS9700 Dialogues of knowledges in research Emneplan

Introduction, aim and content

This course is included in the SIICHER doctoral program and corresponds to the Academic communication II-course in that program.

The course is set within the framework of the Regional Program for the Strengthening of Indigenous, Intercultural and Communitarian Higher Education and Research in Colombia and Abya Yala (SIICHER). This is a collaboration between two indigenous intercultural universities in Colombia and Nicaragua, and OsloMet in Norway.

The course consists of two main components:

• Dialogues of knowledges as a tool to expand scientific ways of knowing and learning
• Academic communication that reflects these dialogues of knowledges (for example an article, a research project, a conference paper, etcetera)

Aim

The aim of the course is to educate researchers to explore the potentials for a dialogue of knowledges and search for epistemological common ground, as well as to challenge existing norms and discourses.

"Indigenous Knowledges" is a term that refers to the diverse and complex knowledge systems that have developed over generations within specific Indigenous communities, often in connection with the land, environment, and cultural practices of those communities. These knowledge systems include traditional ecological knowledge, cultural knowledge, spiritual knowledge, and other forms of knowledge that have been passed down through oral traditions and lived experiences.

It is important to recognize that Indigenous knowledges are not monolithic, but are shaped by the particular histories, cultures, and contexts of each Indigenous community. Moreover, Indigenous Knowledges are often embedded in the languages, ceremonies, and other cultural practices of these communities, and are inseparable from their ways of life.

In contrast to the traditionally dominating philosophies of science in the "West" or Global North, Indigenous Knowledges often have a more holistic and relational view of the world, and prioritize the interdependence of humans, animals, plants, and the natural environment. This contrasts with a more reductionist and mechanistic view of the world that has been dominant in Western science.

However, it is important to recognize that today there are many perspectives within Western science that align well with Indigenous perspectives, and that the distinction between Indigenous Knowledges and Western science is not always clear-cut. For example, there are many researchers who draw on both Indigenous and Western knowledge systems in their work, and who seek to bridge the gaps between these systems in order to develop more effective approaches to complex challenges.

Content

• Academic communication
• Indigenous and communitarian philosophies of science
• Alternative Western/Northern philosophies of science and pedagogies

The PhD candidates will meet and interact with lecturers from a wide range of disciplines and cultures, including Nordic minorities and Latin American Indigenous peoples.

Upon completing the course, the candidates are expected to have gained the following learning outcomes (knowledge, skills, and general competence).

Knowledge:

The candidate

• has acquired knowledge of and insight into both Western (European) and Indigenous (Abya Yala) ways of knowing, and in the way that neither are monolithic, and of how they may interact
• has acquired in-depth knowledge and understanding of theories of science and key theoretical approaches

Skills:

The candidate

• has gained competence in comparing, analyzing, and contributing to developing concepts and ideas in the field of dialogues of epistemologies
• has the ability to reflect on and communicate the research in the field and its development in a broader cultural context
• has acquired sufficient academic knowledge to write a research article that can later be published, that combines Western (European) and Indigenous (Abya Yala) ways of knowledge.

General competence:

The candidate

• Is competent in taking part in collaborative learning methods such as Collaborative Online International Learning (COIL)
• is competent in taking part in collaborative international student and staff-driven joint research
• is competent in taking part in debates in national and international fora.

In today's fast-paced energy industry, predictive maintenance plays a crucial role in ensuring the reliability and efficiency of energy systems. This course is designed to equip students with advanced knowledge and skills in applying probabilistic machine learning techniques to optimize maintenance strategies for energy systems.

The course features a structured progression, starting with foundational concepts in systems engineering and energy systems, transitioning to maintenance engineering, and advancing to probabilistic machine learning techniques such as Gaussian processes, hidden Markov models, probabilistic graphical models, and deep belief networks. Real-world case studies provide hands-on experience, enabling doctoral students to bridge theoretical knowledge with practical applications in predictive maintenance.

Students who complete the course are expected to have the following learning outcomes, defined in terms of knowledge, skills and general competence:

Knowledge:

• Understand the principles of systems engineering and their relevance to energy systems and predictive maintenance.
• Gain a comprehensive overview of energy systems, from fossil fuels to renewables, and the unique challenges they present.
• Acquire advanced knowledge of probabilistic machine learning techniques, including Gaussian processes, hidden Markov models, probabilistic graphical models, and deep belief networks.
• Understand the integration of machine learning techniques into predictive maintenance frameworks and their impact on system reliability.

Skills:

• Apply systems engineering principles to design and analyze energy systems, ensuring efficient integration of predictive maintenance strategies.
• Develop and implement predictive maintenance frameworks tailored to energy systems, transitioning from reactive to proactive maintenance approaches.
• Employ probabilistic machine learning techniques, such as Gaussian processes and graphical models, to model system behavior, predict failures, and optimize performance.

Competence:

• Collaborate effectively across disciplines to design solutions for predictive maintenance in diverse energy system contexts.
• Evaluate uncertainties in predictions and make informed decisions to improve the reliability and efficiency of energy systems through predictive maintenance strategies.
• Demonstrate proficiency in implementing probabilistic machine learning algorithms using Python and relevant libraries for energy system predictive maintenance tasks.

Teaching methods will include lectures, group work and guest lectures from industry personnel.