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The Physical Sciences is led by Professor David Colling and Dr Alexander Richards from Imperial’s Department of Physics. The Department is one of the largest in the UK with an outstanding reputation for excellence in research, undergraduate education and postgraduate training.

This subject will teach you to look at a problem/physical situation – in this case, the phenomena of oscillations – using three different tools:

  • The mathematics of calculus
  • Performing experiments
  • Simulating the situation using a computer

In this intensive subject, you will attend a series of lectures which will provide the fundamental background knowledge that will underpin the week’s work, whilst also experiencing first-hand lecturer-led teaching within a university physics environment.

In the past, students have:  

  • Conducted practical experiments, collected and analysed data.  
  • Encountered new mathematical techniques in a lecture-led teaching session.  
  • Learned the basics of the Python programming language and created computer simulations.  
  • Presented the results of their experiment in a group presentation 
  • Finished the week with a real understanding of oscillations and their importance throughout the natural sciences. 

Week one: Sample session outline

Day one
Introduction to Physics including Calculus, simple harmonic oscillators and damping/forcing
In this intensive session, you will attend a series of lectures which will provide the fundamental background knowledge that will underpin the week’s work, whilst also experiencing first-hand lecturer-led teaching within a university physics environment.
 
Computer Programming including Basic Python
You will then be working in the undergraduate computing suite where you will be taught the basics of computer programming in the chosen language. With the help of mentors, you will be expected to complete the online notes and exercises.
Day two
Computer Programming including Matplotlib, NumPy and SciPy
You will start in the morning back in the computing suite furthering your new programming skills within a physics/science context. You will be introduced to some powerful mathematical and scientific libraries such as, Matplotlib, NumPy and SciPy, that can be used in Python and common physics-related tasks.
 
Laboratory Work
After lunch you will work in teams in a real undergraduate teaching laboratory to utilise the knowledge you will have acquired and record and analyse data while performing two experiments.
Day three
Physics based Computer Simulations and Laboratory Work

This session will start in the laboratory where you will construct a physics simulation of simple oscillations before conducting the final and more challenging experiment.

 
Physics based Computer Simulations

You will then move to the computer suite to model more complex systems. Within your groups, you will begin to create a presentation of all that you have learnt during the week, ready to be presented to the group in the final session.

Day four
Team Presentation Preparation
During the final session, you will work together in groups to complete and rehearse your final presentation.
 
Team Presentations
In the afternoon, you and your group will present your idea to your fellow students, mentors and course leaders. There will be time set aside after each presentation to allow for questions. Presentations will be judged by the mentors and leaders and a prize will be awarded to the winning group.

Week two: Innovation Challenge

In week two of the programme, you will come together with students from other academic subjects to share expertise and to help solve a real-world challenge.  
  
The Innovation Challenge is led by the Imperial Enterprise Lab, a group of industry experts, and is a chance for you to develop vital skills you will need at university such as presentation, communication and group work.  

Find out more about the Innovation Challenge.

Oscillation experiment

Group oscillation experiment

Group programming session

Group programming session

Group discussion

Group planning discussion with help from student mentors

Group data review

The team reviewing the experiment data