Find Academic Supervisors

• Data management and data science
• Electronics, sensors and digital hardware
• Control engineering, mechatronics and robotics
• +4 more

Dr. Scott Adams serves as a Senior Lecturer at Deakin University within the Faculty of Science Engineering and Built Environment, School of Engineering, and is affiliated with the Centre for Sustainable Bio-Products. His research spans multiple engineering disciplines with a strong emphasis on sustainable technologies, artificial intelligence applications, and biomedical device development. Dr. Adams earned his Doctor of Philosophy and dual Bachelor degrees (Engineering with Honours and Information Technology with Distinction) from Deakin University. His academic journey progressed from Postdoctoral Research Fellow (2019-2022) to his current Senior Lecturer position. His research interests focus on data management and data science, electronics and sensor technology, control engineering, mechatronics, robotics, and biomedical engineering . His work demonstrates particular expertise in sustainable waste management systems, AI-driven monitoring solutions, and neural interface technologies. Dr. Adams has developed innovative approaches to tyre recycling, solar panel inspection, greenhouse monitoring, and neural microprobes with adaptive stiffness. An analysis of his recent publications reveals a strong trend toward sustainable engineering solutions with applications in waste recycling (particularly end-of-life tyres and solar panels), AI-enhanced monitoring systems for environmental and agricultural applications, and advanced biomedical devices featuring novel materials and control systems. His interdisciplinary approach bridges mechanical engineering, computer science, and environmental science. Dr. Adams actively supervises numerous doctoral students across diverse engineering projects and has secured substantial research funding through multiple industry collaborations. His grants portfolio includes significant projects such as the Commercialisation of an Automated Mobile EOL Solar PV Panel Recovery Plant ($345,925), End of Life Tyres to Green Hydrogen ($23,745,206), and various contracts with rail companies, energy firms, and agricultural technology developers. His teaching responsibilities include units such as SEE216 - Analogue and Digital Electronics, SEJ102 - Electrical Systems Engineering Project, and SEE711 - Sensor Networks. Dr. Adams has developed several innovative devices including the SmartStim AI-enabled deep brain stimulation device, AgriGlow IoT multi-spectral light sensing system, and automated soil gas monitoring technology, demonstrating his commitment to translating research into practical applications.

Rita Quetziquel Fuentes AguilarView profile

Researcher

• Biomedical Engineering
• Control Systems
• Artificial Intelligence
• +6 more

Rita Quetziquel Fuentes Aguilar is a Researcher at the National School of Sciences and Engineering of Tecnológico de Monterrey, where she leads the Institute of Advanced Materials for Sustainable Manufacturing. She specializes in biomedical engineering applications, from the design of new medical devices to control implementation. Her work spans multiple disciplines including artificial intelligence, robotics, and biomedical engineering. Dr. Fuentes-Aguilar's educational background includes: Bio-medical engineer from Instituto Politécnico Nacional, Ciudad de México Master of Science from Centro De Investigación Y De Estudios Avanzados Del IPN PhD in Automatic Control from Centro De Investigación Y De Estudios Avanzados Del IPN Her research focuses on mathematical model identification and control of objects deformation, machine learning for prediagnosis, and the development of solutions in the medical area. She has made significant contributions to rehabilitation robotics, medical device design, and AI-assisted biomedical engineering. Her work often bridges theoretical control systems with practical medical applications, resulting in numerous patents and publications. Dr. Fuentes-Aguilar's recent publications demonstrate a clear interdisciplinary trend combining control theory, artificial intelligence, and biomedical applications. Many focus on rehabilitation technologies, medical imaging, and adaptive control systems for medical devices, with increasing emphasis on sustainable manufacturing approaches. Her notable scientific achievements include: Mejor Cartel award from Encuentro de Ingeniería Biomédica (2011) Premio a la Excelencia Académica from Instituto Politécnico Nacional (2007) Mexican Researcher Certification - Level 1 Dr. Fuentes-Aguilar has secured research funding through projects sponsored by the National Council of Science and Technology and is a member of the National System of Researchers. She has one granted patent, seven national patent applications, three PCT applications, and three public copyright registries, reflecting her commitment to translating research into practical applications. She leads the Institute of Advanced Materials for Sustainable Manufacturing and is part of the Strategic Research Group "Enabling technologies for the development of advanced materials" in Guadalajara. Her work contributes to UN Sustainable Development Goals including Good Health and Well-being, Industry Innovation and Infrastructure, and Life on Land.

• Scanning Probe Microscopy
• Surface Science
• Nanoelectronics
• +3 more

Dr. Rüdiger Berger serves as Group Leader of the central Scanning Probe Microscopy (SPM) facility at the Max Planck Institute for Polymer Research in Mainz, Germany, operating ten microscopes for 30+ researchers since October 2002. Educational background: Physics degree from Friedrich-Alexander University Erlangen-Nuremberg (1994), specializing in high-Tc superconductors via SPM Doctorate and postdoc at IBM Research Laboratory Zurich (micromechanical sensors with Ch. Gerber/J. Gimzewski) Research engineer at IBM Deutschland Speichersysteme GmbH Mainz (1998-2002), focusing on test system automation and magnetic materials His research investigates nanoscale electrical surface properties with direct applications in solar cells and batteries. Collaborating with the Butt group, he pioneers droplet friction analysis methods and studies interfacial droplet behavior at defects. This work bridges surface science, nanoelectronics, and energy materials through advanced microscopy techniques. Recent publications (2023-2024) demonstrate convergence of operando microscopy, machine learning, and interfacial physics—particularly in droplet dynamics and solid-state battery systems. Key trends include computational analysis of sliding drops, lithium dendrite evolution at grain boundaries, and nanoscale friction quantification, spanning electrochemistry, fluid dynamics, and materials characterization. No scientific awards were documented in the source material. No student advising relationships or research grants were specified in the provided information. Berger manages the SPM facility as a collaborative hub where 30+ scientists utilize ten manufacturer-diverse microscopes for surface/interface characterization. The laboratory enables nanoscale electrical and mechanical property analysis across polymer research domains, emphasizing real-time observation of energy materials and fluid-solid interactions.

Jon StammersView profile

Research Fellow

• Artificial Intelligence
• Data Science
• Smart Manufacturing
• +3 more

Dr Jon Stammers serves as the Senior Theme Lead for Data, Connectivity and AI within the Integrated Manufacturing Group at the Advanced Manufacturing Research Centre (AMRC), University of Sheffield. He joined the AMRC in 2013, initially working in the Machining Group's Process Monitoring and Control team, and has since taken on leadership of the Data, Connectivity and AI theme. Additionally, he has lectured at the AMRC Training Centre and is an active member of the Centre for Machine Intelligence and the IET Manufacturing Technical Network. Stammers holds an MEng and PhD in Electronic Engineering. His doctoral research investigated automated identification of urban and natural audio signals using time-domain feature extraction and ensemble neural network classifiers. His primary research interests focus on leveraging data from connected manufacturing processes to enable Smart Factories. This includes developing open-source data architectures, data visualization techniques, computer vision applications, AI and machine learning algorithms, data security measures, and data science methodologies. He is particularly interested in how AI can serve as a practical tool to enhance daily manufacturing operations and the broader societal impacts of technology adoption in industrial settings. Analysis of his recent publications reveals a consistent emphasis on machine tool health monitoring, anomaly detection in machining processes, and the integration of AI for predictive maintenance. His work bridges theoretical advancements in signal processing and machine learning with practical industrial applications, contributing to more efficient and reliable manufacturing systems. No scientific awards, prizes, or fellowships were mentioned in the provided information. Stammers has previously lectured at the AMRC Training Centre, contributing to workforce development in advanced manufacturing. While no formal PhD or Master's advisees are listed in the provided information, his role in training is evident through his educational contributions. He has secured significant research funding as Principal Investigator and Co-Investigator on multiple projects, including the ATI-funded "Securing Aerospace Manufacture in the UK" (£974,000), Innovate UK's "Data-driven manufacturing" (£111,000), EPSRC's "Autonomous Method for Detecting Cutting Tool and Machine Tool Anomalies" (£1.02M), and several others totaling over £2.5 million. His current projects span AI for machining design, hydrogen storage, and geospatial AI for housing layouts. Stammers leads the Data, Connectivity and AI theme at the AMRC, which focuses on enabling Smart Factories through innovative data and AI solutions. He is part of the Integrated Manufacturing Group, a key research team within the AMRC dedicated to advancing manufacturing technologies.

• UAV photogrammetry
• Remote sensing
• Geomorphological monitoring
• +7 more

Anette Eltner is a Junior Professor for Geosensor Systems at the Technical University of Dresden since 2021. Her work focuses on developing innovative methods for environmental monitoring using AI and remote sensing technologies. She leads research projects that aim to improve flood forecasting systems and monitor geomorphological changes through advanced imaging techniques. Her educational background includes: PhD in Geography from TU Dresden (2016), awarded by the German working group for geomorphology Diploma in Geography from TU Dresden (2010) with minors in Photogrammetry/Remote Sensing, Soil Science and Hydrology Professor Eltner's research spans multiple cutting-edge areas in geospatial technology and environmental science. She specializes in UAV photogrammetry and remote sensing, developing methods for precise geomorphological and hydrological monitoring. Her work integrates artificial intelligence with environmental sciences to create more accurate predictive models for natural processes. She focuses particularly on erosion processes in fragile landscapes and has pioneered techniques for spatio-temporal high resolution topography using laserscanning, structure-from-motion, and time-lapse imaging. Her innovative approach to image processing enables automatic feature detection and tracking in geographic applications, while her development of low-cost geosensor systems makes advanced monitoring accessible in resource-limited settings. Her publication record demonstrates consistent advancement from foundational photogrammetric techniques to sophisticated AI integration for real-time environmental assessment. The research shows increasing emphasis on practical flood warning applications and dynamic landscape monitoring, particularly for small waterways where traditional monitoring systems are lacking. Her notable scientific achievements include: PhD thesis award from the German working group for geomorphology (AK Geomorphologie) TUD Young Investigator status (recognizing excellent, independent junior research group leaders) Editorial board membership for Geoscientific Instrumentation, Methods and Data Systems and Photogrammetric Record Professor Eltner leads the "Artificial Intelligence for Flood Warning (KIWA)" project, evaluating camera-based water level monitoring systems along small tributaries of the Elbe River in Saxony. Her research group develops AI-supported methods to analyze water surface imagery and predict flood events in small waterways. She has secured funding for projects in Germany and internationally, including current work in Oman combining cameras and seismic sensors to monitor rare flood events in wadis. Her team is developing systems that allow sensors to communicate with each other for dynamic environmental monitoring. Her laboratory work focuses on developing cost-effective monitoring solutions using accessible technology like Raspberry Pi computers and standard cameras. She has demonstrated that these low-cost systems can effectively monitor significant landscape changes, making advanced environmental monitoring more widely available. Current projects involve creating 3D point cloud analysis methods that incorporate temporal components to better predict dynamic processes like soil erosion, rockfalls, and landslides.