Consultancy

Need support with your research project?
If you would like learn more about NeSI's Consultancy service and how you can work with NeSI Research Software Engineers on a project, please contact us (support@nesi.org.nz) to set up an initial meeting. We can discuss your needs and complete a Consultancy application form together.
Researchers from NeSI collaborator institutions (University of Auckland, NIWA, University of Otago and Manaaki Whenua - Landcare Research) and those with Merit projects can usually access consultancy at no cost to themselves, based on their institution's or MBIE's investment into NeSI.
What do we do?
Examples of outcomes we could assist with (this list is general and non-exhaustive):
- Workflow parallelisation – allowing more inputs to be processed simultaneously, usually by different instances of the software running side by side
- Software parallelisation – use of technologies such as OpenMP or MPI to process one single input more quickly by one instance of the software
- Code optimisation – redesign of algorithms to improve overall speed or efficiency of resource (CPU or memory) use
- Improving I/O performance – work to speed up reading from or writing to the disk, or to reduce the amount of data that must be read or written
- Porting to GPU – accelerate code by offloading computations to a coprocessor
- Creating/Improving a visualisation workflow - specialised visualisation solutions or developing interactive web-based dashboards
- Translating Python/R/Matlab code to C/C++/Fortran for faster execution
- Improving software sustainability – introducing best practices such as version control and unit testing
- Conducting an Exploratory Data Analysis - use visualisations and statistical methods to summarise the main characteristics of a dataset in light of scientific questions
- Designing and fitting explanatory models - adapt or create statistical models to estimate latent variables distributions from noisy observations and expert knowledge
- Designing and fitting predictive models - train machine learning models to predict variables of interest on unlabeled datasets
- Cleaning data - assist with automating the identification and correction of erroneous values in a dataset
What can you expect from us?
During a consultancy project we aim to provide:
- Expertise and advice
- An agreed timeline to develop or improve a solution (typical projects are of the order of 1 day per week for up to 4 months but this is determined on a case-by-case basis)
- Training, knowledge transfer and/or capability development
- A summary document outlining what has been achieved during the project
- A case study published on our website after the project has been completed, to showcase the work you are doing on NeSI
What is expected of you?
Consultancy projects are intended to be a collaboration and thus some input is required on your part. You should be willing to:
- Contribute to a case study upon successful completion of the consultancy project
- Complete a short survey to help us measure the impact of our service
- Attend regular meetings (usually via video conference)
- Invest time to answer questions, provide code and data as necessary and make changes to your workflow if needed
- Acknowledge NeSI in article and code publications that we have contributed to, which could include co-authorship if our contribution is deemed worthy
- Accept full ownership/maintenance of the work after the project completes (NeSI's involvement in the project is limited to the agreed timeline)
Previous projects
Listed below are some examples of previous projects we have contributed to:
- A quantum casino helps define atoms in the big chill
- Using statistical models to help New Zealand prepare for large earthquakes
- Improving researchers' ability to access and analyse climate model data sets
- Speeding up the post-processing of a climate model data pipeline
- Overcoming data processing overload in scientific web mapping software
- Visualising ripple effects in riverbed sediment transport
- New Zealand's first national river flow forecasting system for flooding resilience
- A fast model for predicting floods and storm damage
- How multithreading and vectorisation can speed up seismic simulations by 40%
- Machine learning for marine mammals
- Parallel processing for ocean life
- NeSI support helps keep NZ rivers healthy
- Heating up nanowires with HPC
- The development of next generation weather and climate models is heating up
- Understanding the behaviours of light
- Getting closer to more accurate climate predictions for New Zealand
- Fractal analysis of brain signals for autism spectrum disorder
- Optimising tools used for genetic analysis
- Investigating climate sensitivity
- Tracking coastal precipitation systems in the tropics
- Powering global climate simulations
- Optimising tools used for genetic analysis
- Investigating climate sensitivity
- Improving earthquake forecasting methods
- Modernising models to diagnose and treat disease and injury
- Cataloguing NZ's earthquake activities
- Finite element modelling of biological cells
- Preparing New Zealand to adapt to climate change
- Using GPUs to expand our understanding of the solar system
- Speeding up Basilisk with GPGPUs
- Helping communities anticipate flood events