NeuroInsight Research

NeuroInsight Research Challenges

Applicants can develop a research project aligned to one or more of the NeuroInsight challenge domains:

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

Programme led by

Royal College of Surgeons in Ireland, 123 St Stephens Green, Dublin 2, Ireland