Programme led by
Royal College of Surgeons in Ireland, 123 St Stephens Green, Dublin 2, Ireland
NeuroInsight Research Challenges
Applicants can develop a research project aligned to one or more of the NeuroInsight challenge domains:
Click on the Projects and Ideas Board to view some of our current projects. Suggestions for other projects from applicants can be made by emailing neuroinsight@rcsi.ie or making contact direct with supervisor.
Ethnographics
Development of precision medicine and methods to capture data from neurological disease.
Advanced Analytics
Using patient data to predict important clinical events including response to therapies.
Genomics
The analysis of genomic data from human and animal models to inform diagnosis and therapeutics.
Patient Impact
Using insights from data to inform more effective and targeted healthcare provision.
Expertise and application areas
Within the above domains, we anticipate projects in the following research fields. Clicking will take you to supervisors working in these domains.
Diagnostics
Applying sequencing and/or advanced informatics and/or nanomaterials technology to discover, detect and interpret biomolecules from patients collected via national clinical networks to deliver faster, more accurate diagnosis
Therapeutics
Exploring new types of therapies, including molecules that work by controlling the activity of gene networks to stabilize or recover brain function, with targeted delivery to the affected area
eHealth
Provide insight to research-adapted continuously learning and innovative healthcare system through careful design, development and implementation of eHealth technologies
Genomics, Bioinformatics and Computational Biology
Developing expertise and capacity in DNA and RNA sequencing, bioinformatics and systems biology
Preclinical Disease Phenotyping
Modelling neurological diseases - in vitro cellular, iPSC, and in vivo-whole animal - to develop capacity in genetic and pharmacologic functional screening and molecular-cellular-animal imaging
Clinical Research and Trials
Enabling advanced clinical infrastructure, including health registers for neurological disease (e.g. epilepsy, ALS etc), as well as the collections of patient DNA and biofluids
Machine Learning and Deep Learning
Computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyse and draw inferences from patterns in data, including 'deep' systems that use layered neural networks to imitate the human brain
Decision Making
Managing increasing volumes of data and incrementally incorporating new data into decision making processes, exploiting virtualized and reconfigurable networks, and scaling the decision support to the most appropriate selection of data
Multimodal Data Analysis
Use of multi-modal data (imaging, video, audio along with traditional datasets) for complex data analytics across data sources ,and interpretation of complex data types
Augmented Human
The integration of the human (through our personal data) into an infrastructure that offers an opportunity of performance enhancements in personal sensing, connected health, data analytics for health, recommender systems, semantic theories and others
Smart Enterprise for Health
Enable healthcare enterprise to have a deeper, more detailed and more dynamic understanding of itself, its offerings and its patients/customers by mining, interpreting and integrating enterprise data repositories and streams, customer data sources and relevant and contextual open (publicly available) data, in order to make better augmented decisions or recommendations
Data Engineering and Data Governance
The complex task of making large volumes of raw data usable to those who might benefit from its inherent value; making data accessible, interoperable, secure and robust, and providing predictive models and finding trends