Skills:
Perspectives from My Group
Obviously different research involves different skills.
Here are some specific things you might learn within my research group.
Focus on My Research Areas
Some of the more specific skills you might gain in a research project with me include:
programming with Python
fast parsing of output files, efficient creation of multiple input files
production of graphs through matplotlib
accessing online information through request libraries
developing a web-based interface through Django
You may want to use an adjective to describe your skills in a particular task. Try "beginner", "competent", "power/ expert" user. Be honest here - it is easy to check and you are much less likely to get the opportunity than if you lie.
Task/ skill may include:
use of computational chemistry software such as --- (please distinguish between your knowledge of computational chemistry methods and your experience with a particular software package - I want to know both, with the former usually being more important to me personally though other supervisors might feel differently)
use of supercomputer clusters like Raijin, National Computational Infrastructure
use of terminal, i.e. command line including potentially bash scripts
If you are doing a more educational or outreach focused project, perhaps you learn about:
how to conduct focus group or interview - qualitative research methods
evaluation of programme
supervision
mentoring
Some more specifics for students in my group:
Literature Review
- Analyse a paper’s usefulness effectively and quickly
o Use abstracts effectively to filter out relevant and irrelevant papers
o Use the papers introduction and conclusion to understand its main points
o Identify key tables/ figures in the paper
- Skills in conducting a thorough Literature Review
o Effective use of key words in Google Scholar and/or Web of Science
o Identifying papers that are and aren’t useful
o Following references & citations
o Identifying key authors in the field
- Critical Analysis of the Literature
- Synthesis of a large body of literature into a few sentences/ short paragraphs
- Identify key breakthroughs/ contributions
- Identify trends in scientific research on your topic
Data Science
- Managing large data sets using effective computational methods
- Analysing large data sets for intended purpose
- Constructing and refining a useful database for purpose
- Critically analysing data taking into account intended purpose
- Document database collation decisions, and reference data sources appropriately
Model construction
- Fitting data to a model to extract meaningful properties; this can be done using Excel’s Solver or Mathematica’s more sophisticated routines
- Displaying pragmatism and judgement in assessing results of initial model, particularly identifying areas of over-fitting and the accuracy of the model
Computational
- Interact with an unfamiliar software package, learning from previous examples, documentation and your own testing
- Identify and correctly utilise small test cases
Programming
- Understand code written by someone else
- Modify existing code for new purpose or to add new features
- Debug code through print statements, commenting etc
Problem Solving
- Break a big problem into smaller steps that can be done by a computer (or yourself (computational thinking)
- Convert a complicated problem into one or more simpler initial problems
- Convert a complicated scientific problem into an easily testable hypothesis
Critical Thinking: Errors
- Understand the sources of error in your methodology
- Identify the relative magnitude of these errors
- Focus on addressing & minimizing the big errors not the small ones
- Identify & take advantage of the fact small errors won’t change the overall answer to save time, effort & mental energy
- Understand & apply the above principles in terms of
o calculation time of codes (either running someone else’s code or writing & optimising your own)
o your own time & project management
Big-picture Perspective
- Understanding how the details of your project fit into the big picture of the scientific research field
- Make decisions on the details of your project based on the big picture use of your data
Writing
- Communicate clearly in emails
- Contribute a paragraph or section to a larger academic paper
- Give actionable constructive feedback on
o overall paper structure
o paragraph structure
o sentence construction
o grammar
o figures
o tables
o use of references
- Write a short individual report using Latex/ Bibtex, using appropriate figures, tables, referencing and section structure.
Communication
- Prepare a good presentation with
o Not too many words on a slide
o Lots of appropriate pictures/ figures/ diagrams etc
o A good overall structure
o A good overall story
o Caters to the intended audience’s interests and background
o Focuses on scientific why & what, including your results, not the how
- Give a talk in front of an audience
o Do the above (it is usually hard the first few times!)
o Adapt to the audience (e.g. appreciate then adapt to when you get signs they do and don’t understand)
o Engage with the audience (allowing them to contribute)
§ Question time
§ During the talk (where appropriate)
- Present research without slides to academic and/or in small groups
o Develop ability to do this concisely & effectively
- Present your research to academic at least weekly in a way that enables
o Effective communication of your progress
o Clear identification of your problems (thus enabling discussion)
Mathematics
- Utilise mathematics in practical applications (method development for quantum chemistry, model development & analysis)
Personal Skills
- Self and time management over long periods of time (days/ weeks)
- Metacognition of one’s own strengths, weaknesses, effective work patterns (e.g. time of day, environment etc)
- Ability to ask clear questions to clarify areas of confusion and to progress research
- Ability to clearly communicate work progress and intended work direction
- Engage with PhD students, post-docs and/or academics, and gain knowledge, insight and/or connections
- Self-confidence in your ability to contribute to science
Self-directed Learning
- Ability to learn for yourself beyond the uni environment; the world changes every day and we need to learn continually to adapt & thrive (besides: it’s fun!)
General Research Knowledge you can gain:
Career Information
- What is a PhD? What does it involve and why is it challenging?
- Academic career path
- What are papers and why are they important?
- How are academics funded; grants etc
- Some options outside academia where you can utilise these skills (I am working on improving my own knowledge and signposting skills here)
o I am strong in telling you about teaching and outreach-related opportunities
Research Appreciation
- Understand how your subject knowledge & skills are applied in a research context
- Understanding how mathematics and computers can be applied to chemistry research
- Understand most modern research is interdisciplinary
Specific programs you can learn:
- Python
- Latex/ Bibtex (type-setting software; beginner pseudo-programming language)
- Unix terminal (text-based commands)
- Bash scripts
- Accessing external computer clusters/ supercomputers, submitting computational jobs etc
- Mathematica
- Advanced Excel skills (particularly Solver)
- Working collaboratively through Google suite (Drive, Docs, Sheets, Slides)
- Fortran
- Electronic structure packages to calculate electronic properties (energies, geometries, dipoles, etc) (particularly Molpro)
- Nuclear motion programs to calculate rovibronic energies and transition intensities (particularly Duo for complex diatomics with coupled electronic surfaces)
- Online databases or programs (DC or MARVEL etc)
There are also excellent opportunities to utilise your knowledge of C++ (ask for method development projects here).
Subject specialist knowledge you can gain:
- Rovibronic spectroscopy
- Quantum Chemistry
- Detailed electronic structure notation
- Computational Chemistry
- Molecular Physics
- Interdiscplinary fields, e.g. applications of small molecules & spectroscopy in
o Astronomy
o Environmental monitoring
o Industry
o Quantum computers
o Ultracold experimental molecular physics
- Innovative education methodologies, particularly
o Research-in-schools
o Integrating research, teaching, outreach & skill development