Dr. A. Cicirello

Dr. A. Cicirello



Alice is Associate Professor and Head of the Section of Mechanics and Physics of Structures in the Engineering Structures Department at TU Delft (2020-present), and currently holds the Alexander von Humboldt Foundation Research Fellowship for Experienced Researchers.

Alice was a Departmental Lecturer in Dynamics and Vibration at the Oxford Engineering Science Department and a Career Development Fellow in Engineering Science at Balliol College (2017-2019). She founded and led the Dynamics, Vibration and Uncertainty Laboratory in the Oxford Engineering Science Department from 2017 to 2021. Prior to these positions, she was a Senior Research Scientist at Schlumberger (2014-2017), a Research Associate (2012-2014) and a Marie Curie Early Stage Researcher (2009-2012) at the Cambridge University Engineering Department. Alice obtained her PhD from the University of Cambridge in 2013.

Alice is the founder and Principal Investigator of the Data, Vibration and Uncertainty Research Group

Alice has held visiting positions at several research institutions, including MIT, Sandia National Laboratories, and the University of Auckland, an Honorary Lectureship at the University of Liverpool and the IAS Open Programme Fellowship at the Institute of Advanced Studies (Loughborough University). Alice has organized and chaired technical sessions at international conferences, technical workshops and online seminar series. Alice was part of the organizing committee of the 10th International Conference on Modern Practice in Stress and Vibration Analysis (MPSVA 2022), and of the scientific committee of several conferences (including ISMA2022-USD2022).  Alice organised and chaired of the Physics-Enhancing Machine Learning in Applied Solid Mechanics Workshop (12/22).

Alice is currently:

  • Visiting Fellow at the Engineering Science Department at the University of Oxford (2020 - present)
  • Advanced Visiting Fellow at the Department of Mechanical Engineering, University of Sheffield.
  • Member of the Scientific committee of UNCECOMP 2023.
  • Member of the Scientific committee of the Platform for Advanced Scientific Computing (PASC) Conference, PASC23
  • Member of the Scientific committee of the European Workshop on Structural Health Monitoring, EWSHM.
  • Member of the local organizing committee of the XII International Conference on Structural Dynamics,  Eurodyn 2023
  • Editorial Board member for the journals:

Data-Centric Engineering
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering,
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

Alice was awarded the Bronze for Excellence in Teaching for the academic year 2018/19 at the Oxford Engineering Science Department.



My research interests span from fundamental to applied research. My technical expertise covers ncertainty Quantification, Machine Learning applied to measurements, text, and physics-based models, advanced physics-based modelling of non-linear systems, Dynamic Experimental Testing and monitoring of components/systems/structures and materials. My research aims at developing effective strategies for guiding, at the design-stage and in operating conditions, decisions making on important functional components, critical structures and complex systems under deep uncertainties and non-linearity.

I work on:

  • fundamental understanding of the behaviour of structures for extracting important physics-based features;
  • digital twins development for (i) the design of structures that are relatively insensitive to manufacturing variability and uncertainty, (ii) assessing the safe remaining life of structures in environmental and operating conditions;
  • effective structural health monitoring strategies and non-destructive testing.

These research challenges are strongly connected and I tackle them with an integrated approach which combines and enhances each of four core aspects:

  • advanced linear and non-linear physics-based models;
  • laboratory experiments and testing campaigns;
  • uncertainty quantification (both forward and inverse);
  • machine learning, signal processing and monitoring strategies. 

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