Sort by
Refine Your Search
-
Listed
-
Country
-
Field
-
project focused on the development machine-learning powered digital twin system for the structural performance of civil engineering structures. The project is a collaboration between multiple research
-
advanced machine learning and deep learning tools to decode the complexity of immune–tumor interactions, integrate multi-omics data at scale, and predict patient responses to therapy. The center works at
-
road conditions. Your specific activities will include (but are not limited to): • Develop robust, production-grade machine learning solutions for predictive modelling and complex decision
-
monitoring. Design and implement machine learning models to analyze multimodal data (e.g., student behavior, engagement, and performance) to enhance personalized learning. Develop and evaluate GPT-powered AI
-
(e.g., finite element or wave propagation simulations) for defect detection and materials analysis Integrate AI, machine learning, and robotics into NDE and manufacturing processes for automation and
-
experience in the analysis of metagenomics and/or biological high-throughput data Knowledge of statistical and machine learning methods in the context of biological systems Experience with programming (e.g
-
Established Researcher (R3) Positions Postdoc Positions Country Ireland Application Deadline 11 Sep 2025 - 23:45 (Europe/Dublin) Type of Contract Temporary Job Status Full-time Hours Per Week 39 Is the job
-
. The department has a strong community on related topics: research groups working on digital health and wellbeing , network science , computational social science , and various topics in machine learning. You will
-
data Experience with GIS/RS and database environments (e.g., ArcGIS and Quantum GIS) Experience with machine learning and statistical learning Experience working with large, diverse datasets Familiarity
-
decision-making for complex infrastructure systems. This position offers an opportunity to contribute to interdisciplinary research at the intersection of civil engineering, machine learning, and systems