CHL5230H Position Description -Emergency Posting
Machine Learning, a branch of Artificial Intelligence, has emerged as a fundamental methodology to support Data Science applications. This is particularly prominent in health and medicine, in both research and health care applications. This course will introduce the fundamental concepts and methods of Machine Learning when applied to health and biomedical data. The time will be divided between the presentation of theoretical background and the opportunity for hands-on practical application using the R statistical software and a variety of health data.
Instructor: Nicholas Mitsakakis
Qualifications:
Educational Background: PhD student in Biostatistics, Statistics, Epidemiology, Computer Science, or other related quantitative fields, with strong statistical background. Prior experience as a TA, or instructor in statistical, computational or related fields is beneficial.
Proficiency with R: Strong coding skills in R, especially in the context of data manipulation and machine learning.
Machine Learning Expertise: Solid understanding of foundational machine learning algorithms and concepts, including supervised and unsupervised learning, feature selection, model evaluation, and optimization.
Familiarity with health data: Previous exposure to health data, and understanding of basic epidemiology concepts and study design is important
Communication Skills: Ability to explain complex technical concepts in simple terms to students with varying levels of expertise.
Problem-solving Skills: Ability to assist students in debugging code and understanding machine learning problems and solutions.
Time Management: Ability to manage multiple tasks, such as grading, holding office hours, and responding to student queries in a timely manner.
Duties: Marking assignments and exams
Office Hours: Schedule and conduct regular office hours to address student queries and provide additional guidance on course content.
Communication: Promptly respond to student in order to address questions, clarify concepts, and facilitate discussions.
Feedback: Provide detailed and constructive feedback on assignments in order to promote understanding and improve performance.
Lecture Attendance: Not needed, but it is recommended
Relevant Criterion:
Previous experience is the more relevant criterion than the need to acquire experience in respect of this posted position.