Artifical Intelligence

Nimbus has a unique blend of software engineers, electronics engineers and academic researchers who work in various areas of Artificial Intelligence and Data Analytics.


Artificial intelligence (AI) brings with it a promise of genuine human-to-machine interaction. When machines become intelligent, they can understand requests, connect data points and draw conclusions. Data scientists in Nimbus can build, train, and deploy models with frameworks or choose to benefit from the speed of in-database machine learning.

AI Domains

We have had many successful projects in AI domains such as:

  • Stochastic (Deterministic) Optimisation
  • Machine Learning (ML)
  • Constraint Modelling
  • Natural Language Processing (NLP)
  • Data Analytics
  • Knowledge Representation
  • Motion, Perception, Representation
  • Computer Vision
  • Metaheuristics (Non-Deterministic) Optimisation

Asset Scheduling

Proper scheduling is a critical foundation for any successful project/ operation. This is why automating the scheduling process from manual to fully automatic through an optimised scheduling algorithm can improve your organisation. By factoring in skill sets, location, and previous interactions, this automated scheduling software ensures faster response times, increased first time fix rates, and improved customer satisfaction. By utilising up-to-date GPS tracking and cloud-based computing, routes can be calculated instantly, tools and inventory tracked in real-time and complete visibility to technicians, managers, and the whole business operation.

Time Forecasting

Time series forecasting is an important area of machine learning that is often neglected. It is important because there are so many prediction problems that involve a time component. These problems are neglected because it is this time component that makes time series problems more difficult to handle.

Anomaly Detection

Anomaly detection is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically, anomalous data can be connected to some kind of problem or rare event such as e.g. bank fraud, medical problems, structural defects, malfunctioning equipment etc. We are able to pick out which data points can be considered anomalies, as identifying these events are typically very interesting from a business perspective.

Recommender Settings

A recommender system is a subclass of information filtering system that seeks to predict the “rating” or “preference” a user would give to an item. These systems can operate using a single input, like music, or multiple inputs within and across platforms like news, books, and search queries. There are also popular recommender systems for specific topics like restaurants and online dating. Recommender systems have been developed to explore research articles and experts, collaborators, financial services and life insurance.

Data Analytics

How Nimbus Processes Big Data

Over forty researchers from Nimbus with over 160 industry partners are working to ensure Ireland is at the heart of global data analytics research. Nimbus is committed to research that has a positive impact on society and are committed to protecting the rights of the citizen.

Nimbus offers data analytics solutions for a broad range of industry partners in ICT, healthcare, agriculture, finance, environmental, and public services. Nimbus’s expertise includes the whole data value chain from the integration of multiple heterogeneous data sources, to discovering patterns and trends in data and making sense of them.

These include using data to:

  • Develop products and services based on matching the short and long-term needs of individuals and organisations to a real time picture of information, opportunities, and services.
  • Understand customer behaviour to increase customer satisfaction, experience and loyalty.
  • Drive recommendations and support decision making.
  • Find optimal solutions to complex problems; and automate business processes.

Research Areas

  1. Linked data
  2. Semantic web
  3. Machine learning and statistics
  4. Media analytics
  5. Personal sensing
  6. Optimisation and decision analytics
  7. Recommender systems