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AI and data science, particularly in dynamic settings where observations are collected sequentially and decisions influence future outcomes. This project will develop novel machine learning and
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Join the Responsible Machine Learning (ML) Group at the Faculty of Computer Science. Led by Prof. Dr. Martin Pawelczyk, who recently joined the University of Vienna from Harvard University, our research
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BMS constraints. Experience with system identification, uncertainty-aware modelling, large datasets, and machine learning. Evidence of research capability through a thesis, publications, conference
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theoretical modelling. The project will focus on self-learning active mechanical networks, but will be tailored to align with the interests and expertise of the successful candidate - we will mutually ensure
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evidence to support the evaluation of AI and machine learning models. This may include investigating data-centric AI strategies, such as data quality assessment, annotation refinement, dataset curation, and
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systems are generally ill-conditioned. The project sits at the intersection of classical numerical analysis, scientific machine learning and computational chemistry. Based on regularization techniques and
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, relating to craniofacial identification research and machine learning. You will require a computer science background. You will be applying AI and/or machine learning to Face Lab processes in relation
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machine learning and AI methods to develop clinical decision support systems for high-flow nasal cannula therapy. The project: Concerns around the possible negative consequences of delayed escalation
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A Doctoral Researcher (PhD student) in Machine Learning for Electron–Phonon Interactions and Wannier-Based Hamiltonians Aalto University is where science and art meet technology and business. We
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Campylobacter disease burden is assessed, identify drivers of change and possible interventions. You will explore how genomic diversity relates to clinical outcomes, whether machine‑learning approaches can