14 machine-learning PhD scholarships at University of Twente (UT)

PhD Position in Machine Learning for Digital Fincance

total. In your project, you will do research on and apply machine learning techniques to make a real-world impact in academia and at financial institutions. A potential research topic is the design of

PhD position on analysis of geometric machine learning methods

answering mathematical questions. You have a solid background in one or more of the following: functional analysis, numerical analysis, differential geometry, theoretical machine learning. You are motivated

PhD Position in Machine Learning for Green Credit Scores

cannot be overstated. Using the power of Machine Learning (ML) models, our project will examine financial investments and credit risk indicators. While these AI techniques have found widespread application

PhD position Reinforcement learning for digital finance

for explainability, regulatory compliance, model abstractions, and human judgment. We will also examine technological challenges like digital twin environments, machine learning pipelines, and digital finance

PhD positions on Industry 5.0 in manufacturing systems

control systems. You have skills in data analytics/machine learning: proficiency in data processing, analysis, and interpretation to develop predictive models and optimize manufacturing processes. You have

PhD position on innovative metrology techniques for high power laser applications

-based monitoring approach to characterize melt pool spatio-temporally as well as spectro-thermally, Develop algorithms for sensor fusion using Artificial Intelligence/Machine Learning concepts to optimize

PhD position on Sparse Training for Deep Reinforcement Learning

present research findings in top-tier conferences (e.g., Machine Learning, JMLR) and journals (e.g., NeurIPS, ICLR, ICML, IJCAI, AAMAS, ECMLPKDD). Collaborate with a international team of researchers and

PhD position Reinforcement learning for digital finance

for explainability, regulatory compliance, model abstractions, and human judgment. We will also examine technological challenges like digital twin environments, machine learning pipelines, and digital finance

PhD position on Embedded Neuromorphic Processor Architecture with On-Device Adaptation

consumes several orders of magnitude higher energy than the honey bee's brain. Neuromorphic devices are seen as the way forward towards more effective and more efficient machine learning. However, current on

PhD position on Sparse Training for Energy-Efficient Deep Learning

methods to scale up deep learning. Publish and present research findings in top-tier conferences (e.g., NeurIPS, ICLR, ICML, IJCAI, AAMAS, ECMLPKDD) and journals (e.g., Machine Learning, JMLR). Collaborate