Sept 2016: New PhD Scholarship
September 2016: PhD position in Data Mining, Machine Learning and Artificial Intelligence
Post Reference: DMML-ROC-16B
A new PhD position is available in the Data Mining & Machine Learning Research Group, led by Dr Michael Madden in the National University of Ireland Galway.
The successful candidate will join the Data Mining & Machine Learning Group and contribute to a new research project, ROCSAFE (see below) funded by the European Union’s Horizon 2020 Programme. The research is likely to involve one of: (1) advances in temporal Bayesian reasoning for decision support; (2) routing of autonomous vehicles for optimal collection of multi-resolution image and sensor data; (3) context-aware decision support driven by sensor data analytics.
The successful candidate will receive a tax-free scholarship of €18,000 per year for 3 years, with fees also paid and additional funding available for research-related travel.
This is an excellent opportunity to combine research with software development, working in a flexible and stimulating research environment, while collaborating with academic and industrial partners across Europe, with occasional travel to meet partners and to attend research conferences.
Information about the research group is available at http://datamining.it.nuigalway.ie/ .
Entry Requirements: Candidates must have a strong honours bachelor degree (equivalent to H1) in Computer Science, Software Engineering, Mathematics, or a similar discipline, and preferably a relevant MSc degree also. Candidates must have excellent mathematical ability, strong software development skills, great communication skills, and a strong interest in research. Preferably, candidates should demonstrate prior experience in data mining and machine learning.
Expected Start Date: 1 Oct 2016 or as soon as possible thereafter.
The positions will remain open until filled, but first consideration will be given to those received by 20 September 2016.
About the ROCSAFE Project: ROCSAFE (Remotely Operated CBRNe Scene Assessment & Forensic Examination) has recently been funded by the European Union’s Horizon 2020 programme. Led by Dr Michael Madden in NUI Galway, it will make advances in autonomous robotics, probabilistic reasoning, intelligent decision support, and miniaturised sensors, all of which will work together to gather forensic evidence in the event of a chemical, biological, radiation/nuclear or explosive (CBRNe) incident. ROCSAFE’s overall goal is to fundamentally change how CBRNe events are assessed, and ensure the safety of crime scene investigators, by reducing the need for them to enter dangerous scenes to gather evidence. There are 13 partners in total involved in the project across Ireland, Italy, Portugal, Spain and Germany, along with a further group of advisory board members. There is more information at http://www.nuigalway.ie/remoteforensics/.
Our research is focused on new theoretical advances in machine learning and data mining, motivated by important practical applications, on the basis that challenging applications foster novel algorithms which in turn enable new applications.
Specific research topics include:
- Artificial intelligence, data mining & machine learning
- Algorithms for classification and numeric prediction
- New methods for combining domain knowledge with data mining
- Time series data analysis
- Probability, reasoning under uncertainty, and Bayesian networks
- Reinforcement learning
- Practical applications of data mining and machine learning in science, engineering & medicine.
Software for Bayesian Network Classification
Over several years, I have developed open-source machine learning software for inductively learning various forms of Bayesian networks from data, and for classification using BNs.
- Learn Naive Bayes, Tree-Augmented Naive Bayes (TAN), General Bayesian Networks, and BN-Augmented Naive Bayes structures
- Multiple scoring metrics, including the K2 metric, BDeu and CMI
- Visualisation of structures using GraphViz (DOT and DOTTY)
- Supplementary code to work with data from other formats, to perform cross-validation, to construct learning curves, and to construct ROC curves
Please cite that paper if you use this software in your work.