This course will support students through the process of writing a journal paper. Each week will follow the structure of a typical science journal paper – with time to network and discover different ways to write in your own time.

Participants are encouraged to meet outside the scheduled sessions to continue working on their paper.

The SUPA industry skills course is delivered by representatives from industry and focusses on essential skills for future careers.  We are very grateful to lecturers for making their time available to contribute to the course.

In the past the course has been put together in collaboration with industry partners including Optos, Thales, Coherent, Leonardo, Marks and Clerk, and the National Physical Laboratory. Partners for the 2020 course are currently being finalised.

The course usually runs by video conference in May/June and is open to all SUPA students, research and academic staff.  

While we encourage student participants to take the opportunity to attend the full course, we recognise that not all lectures may be relevant to all participants, therefore you will be asked to indicate the individual lectures that you will attend using the questions included in each lecture description.

For students, professional development training credit will be given for lectures attended.

Further lectures and topics will be added when finalised.

This is a sign-up / waiting list for anyone interested in the Hands On Writing course.

Due to limited places, we ask you to enrol into this course page, to allow us to collect a list of people interested in this course.

Names will be selected from this list to offer places on the course with priority being given to students in later years of their PhD, with names being drawn randomly thereafter.

This course is taught via a series of lectures running in the SUPA VC rooms at each University 

Course summary:
This course will provide a comprehensive introduction to the principles and practice of advanced data analysis, with particular focus on their application within the physical sciences and on the (rapidly growing) use of Bayesian Inference methods.  Over the past few decades Bayesian inference methods, as a powerful tool for analyzing data, have been growing ever more common across a diverse range of fields of physics. Bayesian inference provides a natural framework in which to address key quantitative questions, constrain the parameters of physical models and measure how well competing models can describe the available data. They also provide an objective and straightforward framework in which to incorporate prior information about those models, obtained e.g. from previous analyses or from theory. Moreover, recent advances in computational methods also offer simple algorithms in which to implement Bayesian methods – even with very large and complex data sets – on a standard desktop computer.  These lectures will give a comprehensive introduction to Bayesian inference methods. The lectures will include some practical exercises designed to introduce some useful codes and algorithms – as well as to showcase the vast array of online resources available to support the “virgin Bayesian” seek to apply these methods to their data.

Lecturer: Ik Siong Heng
Institution: Glasgow
Hours Equivalent Credit: 10
Assessment: Continuous Assessment via series of multiple choice questions.   Optional mock data challenge also available, although not compulsory.
Lecturer:  Michael Alexander
Institution: Glasgow
Hours Equivalent Credit: 8 (4 lectures & 2x2hour tutorials)
Assessment: Assignment Problem

Course Summary:
This course serves as a first introduction to the powerful, object- oriented scripting language Python, which combines ease of use with extensive functionality and simple extensibility. After completion, it’s intended that users will be familiar with the concepts and philosophy of Python, be able to use it to solve a wide range of everyday problems, and be able to extend its functionality with user defined classes and modules for more specialised problems.

Lecturer: Various

Hours Equivalent Credit: 2 days (14hours)
Schedule: 23/24 June 2021
This course will be organised by the Researcher Development Department at the University of Glasgow.

Course Summary
This two day course will provide an insight into the process of
research commercialisation, starting a business, finding funding and

This course will be organised by the Researcher Development Department at the University of Glasgow.

This two day course is aimed at researchers with an interest in:

‧Knowledge exchange, research impact and winning funding for academic career progression

‧Exploring the commercial possibilities of a research idea and how your research might attract industrial funding or be used in setting up a spin-out company

‧Future employment in industry

‧Collaboration with researchers from other disciplines

The course is a mixture of practical activities and case studies. It includes talks from experts and entrepreneurs with inspiring stories and first hand experience of bringing exciting ideas to life. Our speakers will share their knowledge of:

‧Creative thinking and what being enterprising means to them

‧Business planning and different models of research commercialisation (including spin-outs and licensing)

‧Protecting your ideas and intellectual property

‧Compelling and convincing communication, that helps you to bring others on board and win funding

‧How to inspire and motivate others, whether you see yourself as working in business or building a research group

‧Sources of support, advice and funding and how to deal with set-backs

‧How to develop a network

Lecturer: Norman Gray and Daniel Williams
Institution: Glasgow
Hours Equivalent Credit: 2 full days

Course Summary
Many researchers need to write (computer) code of some type or other, though typically as an auxiliary activity – researchers should not turn into ‘programmers’. It is useful for researchers to do that part of their work effectively, now and in the (transferrable) future.  The Software Carpentry course (SWC) aims to instil pragmatic good practice in scientists.

The course covers effective use of the Unix shell and configuration management tools, code testing, and software revision control tools. This is not a programming course, as such, and there are no prior requirements, but a very basic familiarity with Python would probably be useful for some parts of the course.

This will be an online course taking place online, on 5 and 12 March 2021.

Lecturer: Marialuisa Aliotta
Hours Equivalent Credit:

Course Summary

The course is specifically tailored to PhD students in scientific disciplines. It will provide practical tools and strategies to help students understand key elements of good scientific writing. The course will cover the 5 steps of the writing process, from pre- writing to proofreading, and will focus on the structure and style of good academic writing.  Topics covered will include: purpose and structure of different sections (Introduction, Methodology, Data analysis and Results, Discussion and Conclusions, Abstract); use of language and grammar (parallel sentences, appropriate tenses, sentence coordination); supporting materials (figures and tables, bibliography, appendices).