Find Academic Supervisors

• Biomechanics
• Sports Science
• Sports Performance
• +7 more

Cody Lindsay serves as an Associate Lecturer in the College of Nursing and Health Sciences and holds dual affiliations as a Full Member of the Caring Futures Institute and Associate Member of the Medical Device Research Institute. His research bridges sport and exercise technology with real-world performance optimization through industry collaborations. His academic credentials include: Doctor of Philosophy in Sports Biomechanics, University of Canberra (2020-2024) Bachelor (Honours) in Sports Studies, University of Canberra (2019) Bachelor in Sport and Exercise Science, University of Canberra (2016-2018) Lindsay's research centers on applied biomechanics and movement science, utilizing three-dimensional motion capture, force platforms, and wearable sensors to enhance athletic performance and clinical rehabilitation outcomes. Key focus areas include sports technology development, injury prevention, and accessible movement analysis across athletic and health contexts. His 12 publications (2020-2025) predominantly analyze cricket bowling biomechanics, with emerging work in orthopedics and running physiology. This corpus demonstrates consistent application of advanced motion analysis to solve practical challenges in sports performance and clinical rehabilitation. No scientific awards are documented in the source materials. Lindsay supervises research across biomechanics, artificial intelligence, sport science, rehabilitation, and para-sport domains, supported by active industry partnerships that facilitate translational research projects. His institute affiliations provide critical infrastructure for developing sports technology applications, particularly through the Caring Futures Institute's health innovation ecosystem and the Medical Device Research Institute's engineering capabilities.

Clélia de MulatierView profile

Assistant Professor

• Statistical Physics
• Information Theory
• Computer Science
• +5 more

Clélia de Mulatier is an Assistant Professor at the University of Amsterdam , affiliated with both the Institute for Theoretical Physics and the Informatics Institute . She leads research at the intersection of statistical physics, information theory, and computer science , focusing on theoretical and numerical methods for complex systems . Her work spans collaborations with experimentalists in neuroscience and biology , and she actively participates in educational programs across multiple Dutch universities. Research Labs : Computational Soft Matter Lab, Computational Science Lab Affiliations : Dutch Institute for Emergent Phenomena (DIEP), Netherlands Platform Complex Systems (NPCS) Her research develops minimally complex spin models for high-order data analysis , applying exact Bayesian model selection to uncover hidden variable communities in binary datasets. This work has produced open-source tools like MinCompSpin and MinCompSpin_Greedy for different system sizes. Publications demonstrate expertise in tensor networks for dimensional reduction , epidemic modeling , and branching random walks in confined environments . Teaching includes Python programming , complex systems theory , and statistical inference for physics students across multiple institutions. She serves as program committee member for International Conference on Computational Science and organizes academic discussions through initiatives like Behind the CV: story from a Physicist .

Piotr Jan GarbaczewskiView profile

Professor

• Mathematical Physics
• Quantum Physics
• Theoretical Physics
• +7 more

Piotr Jan Garbaczewski is a Professor of Physics at the University of Opole, Poland, where he serves as the Head of the Institute of Physics. Previously, he held professorial positions at the University of Zielona Góra (1998-2006) and the University of Wrocław (1971-1998). His research spans multiple areas of theoretical and mathematical physics, with particular focus on: Stochastic processes and random dynamics Quantum mechanics and fractional quantum mechanics Non-equilibrium statistical mechanics Lévy flights and stable processes in bounded domains Diffusion processes with non-conservative drifts Mathematical aspects of quantum potentials Garbaczewski's recent publication record shows consistent output in prestigious journals, with notable work on killing versus branching processes, non-conservative diffusion, and Lévy processes in bounded domains. His research demonstrates a strong connection between stochastic processes and quantum mechanical systems, particularly through the lens of non-Hermitian quantum mechanics and fractional calculus. His scientific achievements include: h-index (WoS Citations): 7 Total Impact Factor: 31.499 Total SNIP: 15.42 Total CiteScore: 28.41 Total ministerial score: 1,210 Throughout his career, Garbaczewski has maintained active collaborations with researchers across Poland and internationally, contributing significantly to the field of mathematical physics through both theoretical developments and practical applications of stochastic processes in physical systems.

• Spatial Audio
• Machine Learning for Audio
• Environmental Soundscapes
• +3 more

Dr. Marc C. Green serves as a Postdoctoral Research Fellow within the School of Science, Engineering & Environment at the University of Salford, affiliated with the Acoustics Innovation Institute. His research bridges acoustic engineering, machine learning, and environmental psychology with focus on unmanned aircraft systems noise impact assessment. PhD in Music Technology, University of York (2020) Dr. Green's research investigates how drone noise interacts with urban soundscapes through innovative methodologies including soundwalk assessments in controlled environments, psychoacoustic laboratory testing , and deep learning model development . His work specifically examines time-variant noise characteristics, flight operation impacts, and environmental interactions (such as pine tree barriers in urban parks) to understand human annoyance responses. The integration of spatial audio techniques with machine learning enables novel approaches to predicting UAV sound affect beyond traditional sound pressure level metrics. Analysis of his 11 recent publications (2024-2025) reveals a strong focus on practical solutions for urban drone integration, with 55% dedicated to psychoacoustic modeling, 27% to field assessment methodologies, and 18% to technical noise characterization. His work consistently addresses the tension between societal benefits of drone technology and community noise impacts. EPSRC connected nation pioneers prize for 'Creative Computing for the Digital Economy' (for EigenScape database development) Currently leading the RefMap project to reduce UAS environmental impact, Dr. Green's research receives direct institutional support through the Acoustics Innovation Institute. His EigenScape database continues to serve as a foundational resource for global soundscape researchers. The systematic review framework he developed for UAS noise impact testing provides regulators with evidence-based assessment protocols. Dr. Green operates within the Acoustics Innovation Institute's collaborative ecosystem, leveraging specialized recording equipment and computational resources. His research group maintains comprehensive drone noise datasets across multiple platforms and develops open methodologies for community noise assessment in evolving urban air mobility scenarios.