Call for Tutors: IMS Short Courses

The IEEE I&M Society is working on a 4-module short course program on Instrumentation and Measurement with the aim to give people a framework that they can apply to all types of instrumentation and measurement. It provides young professionals, graduate students, and practicing technical staff with increased capabilities in instrumentation, measurement, and calibrations skills. 

The IMS is actively seeking to recruit experienced tutors to support the development and delivery of the first module on Measurement Theory in the Short Course program.

Nominating and Appointing IEEE IMS Tutors

The committee shall evaluate the experience, values, and merits of each nominee based on qualitative and quantitative evidence. Self-nominations are permitted. The nomination package should include:

1. A complete CV including publications and teaching experience, with particular focus on online lecture delivery      
2. Statement of interest from the candidate expressing their willingness to serve as an IMS Short Course Tutor and how they plan to complete their respective duties according to the Guidelines
3. A short lesson resume (5 min), recorded according to the quality and format standards described in the Guidelines

All nominations should be submitted by June 30. The appointment will be made by July 15 and the lesson package, as described in the Guidelines, must be delivered by September 15.

Nomination/Application Form

IMS Short Course Tutor Guidelines

Duties of IEEE IMS Tutors

• Develop one or more lessons included in the topic list. Each lesson includes:
  • A 20-minute video lecture over Powerpoint slides;
  • Working examples and/or work papers, either totally or partially solved by the Tutor;
  • An Assessment Quiz.
• Provide an e-mail address to be used as a primary communication channel with the students asking for clarifications.
• Commit to answering to students’ requests in a timely manner.
• Provide a list of references for each lesson.

Topic list

1. Measurement uncertainty: Risk and cost, direct and indirect measurements, probabilistic uncertainty models, GUM
2. Basic statistics for probabilistic uncertainty model 1: mean, standard deviation, and variance, median, mode, pdfs examples, degrees of freedom, percentiles, regression
3. Basic statistics for probabilistic uncertainty model 2: repeatability, reproducibility, confidence intervals, correlation, generating functions, convolution, central limit theorem, software tools
4. Uncertainty Type A: experimental estimates of uncertainty, standard deviation, normal distribution and probabilities, confidence intervals, prediction intervals, degrees of freedom
5. Uncertainty Type B: theoretical estimates of uncertainty: sources, assumptions, constraints, other sources of data/physical models, non-normal distributions.
6. Total Uncertainty: propagation of uncertainty, combining uncertainties, effective degrees of freedom, Welch-Satterthwaite formula, sums of random variables, convolution, method of moments distribution, sums of central moments, central limit theorem. 
7. Tools to determine total uncertainty: Monte Carlo, numerical toolboxes from TC-32, GUM-tree, Excel
8. Design of experiments: Optimal experiments with low uncertainty, calibration issues, excitation signal design, noise and anti-aliasing filters, frequency band selection methods. 

The full list of guidelines and requirements can be found here.