جستجوی استاد راهنمای دانشگاهی

• Quantum Computing
• Quantum Thermodynamics
• Machine Learning
• +۳ مورد دیگر

Natalia Ares is an Associate Professor in the Materials Department at the University of Oxford and a Tutorial Fellow at New College. She holds a Royal Society University Research Fellowship and joined Oxford in 2013. Her research focuses on advancing quantum technologies through machine learning for device control and quantum thermodynamics. Education: PhD in quantum computing (silicon-based devices) from CEA Grenoble, France Undergraduate and Master's in Physics with specialization in quantum chaos theory from University of Buenos Aires, Argentina Research Focus: Dr. Ares develops machine learning algorithms to control quantum circuits in real-time, addressing device variability challenges. Her group explores quantum thermodynamics at nanoscales, fabricating devices cooled to millikelvin temperatures to study information-thermodynamics relationships, aiming to build autonomous quantum machines like quantum engines and learning circuits. Current projects include machine learning for quantum control and thermodynamics of quantum information processing. Publication Trends (2024-2025): Recent works demonstrate strong integration of machine learning with quantum physics, focusing on quantum-dot systems, thermodynamic costs of quantum operations, symplectic learning frameworks, and high-fidelity qubit control. Publications consistently bridge theoretical quantum physics with practical engineering solutions. Awards and Recognition: Marie Skłodowska-Curie Fellowship Royal Society University Research Fellowship European Research Council Starting Grant (2020) Research Leadership: Leads the Machine Learning research group developing algorithms for quantum control. Manages experimental labs fabricating nanoscale quantum devices operating at cryogenic temperatures. Secured significant funding including ERC Starting Grant for quantum thermodynamics research.

Daniel Tesfamariam Semereمشاهده پروفایل

دانشیار

• Manufacturing Systems
• Machining Processes
• Quality Prediction in Production
• +۳ مورد دیگر

Daniel Tesfamariam Semere is an Associate Professor at KTH Royal Institute of Technology, affiliated with the School of Industrial Engineering and Management (ITM) and the Department of Production Engineering under the Digital Futures Faculty. His role includes teaching and coordinating courses in industrial production systems, such as Digital Factories and Modeling and Simulation of Industrial Processes. He is actively involved in academic administration as an examiner and course manager for several advanced-level programs. His research focuses on manufacturing systems, machining processes, and quality prediction, with notable contributions to tool wear analysis, residual stress prediction, and variation propagation modeling. His work integrates advanced methodologies like dual quaternions, machine learning, and optimization algorithms to address challenges in precision manufacturing and supply chain logistics. Dr. Semere’s recent publications emphasize data-driven approaches to improve manufacturing accuracy, reduce distortions, and optimize warehouse systems. His expertise bridges theoretical models with practical applications, contributing to sustainable and efficient industrial solutions.

• Machine Tool Accuracy
• Advanced Materials Machining
• Manufacturing Processes Optimization
• +۳ مورد دیگر

Fabien VIPREY is an Associate Professor at École Nationale Supérieure d'Arts et Métiers (ENSAM), leading the HSM Team. His research focuses on precision manufacturing technologies, particularly geometric error compensation in CNC machine tools, advanced materials machining (e.g., Inconel 718), and surface integrity analysis. He specializes in developing real-time error correction strategies and in-situ measurement methodologies for multi-axis machining systems. Key Research Areas: Mechanical behavior modeling of parallel kinematic machines Thermal-mechanical distortion analysis in thin-walled components Tool wear effects on machining accuracy and surface quality Development of novel calibration standards for machine tool evaluation His recent work emphasizes real-time feedback control systems for improving volumetric accuracy in 5-axis machining. He has contributed to international collaborations on aerospace material processing, particularly Inconel 718, combining experimental measurements with advanced simulation techniques. Key Publications Trends: Recent articles (2021-2025) concentrate on: Geometric error compensation strategies for multi-axis CNC systems High-feed milling of additive-manufactured Inconel 718 In-situ distortion measurement for thin-walled workpieces Novel multi-feature bar designs for machine tool calibration No awards or active PhD supervision are listed in his profile. His work bridges fundamental mechanical engineering research with industrial applications in precision manufacturing.

• Manufacturing Engineering
• Materials Science
• Mechanical Engineering
• +۳ مورد دیگر

Guillaume Pot is an Associate Professor and Head of the Laboratory POT at the École Nationale Supérieure d'Arts et Métiers (ENSAM). His research focuses on advanced machining processes, materials behavior during cutting, and precision manufacturing technologies. Key areas include geometric error compensation, surface integrity analysis, and thermomechanical modeling in machining operations. Research interests emphasize high-performance materials like Inconel 718, tool wear mechanisms, and distortion control in thin-walled components. Methodologies involve experimental measurements (e.g., infrared thermography, DIC), numerical modeling, and process optimization for industries requiring high precision. Publications span 20+ years, with recent work addressing real-time error compensation, burr formation in superalloys, and clamping effects on material distortion. His work bridges fundamental material behavior studies with applied manufacturing challenges, contributing to both academic and industrial advancements.

• Robotics
• Mechanical Engineering
• Mechanism Design
• +۳ مورد دیگر

Dr. Mathias Hüsing is an Adjunct Professor at the Institute of Mechanism Technology, Machine Dynamics and Robotics within the Faculty of Mechanical Engineering at RWTH Aachen University. His research focuses on robotics, compliant mechanism design, and energy-efficient mechanical systems, with applications in surgical manipulation, additive manufacturing, and human-robot interaction. Research Interests: Robotics (specifically compliant grippers, parallel kinematics, and hybrid robots) Energy-efficient mechanism design (cam systems, dynamic balancing) Smart manufacturing (digital tool chains, multidirectional additive manufacturing) Human-robot collaboration (inclusive workplaces, task adaptation) Sensor integration (calibration methods, non-contact safety systems) Algorithm development (motion planning, optimization techniques) Trends in Publications show a focus on surgical robotics, human-robot collaboration, and additive manufacturing. His work combines mechanism theory with practical implementation, emphasizing industrial applications and energy efficiency. Institute Affiliation includes leadership roles at the Institute of Mechanism Technology, Machine Dynamics and Robotics. He actively develops educational tools like the Software Mechanism Developer and integrates robotics into Industry 4.0 frameworks.

• Control Theory
• Marine Cybernetics
• Robotics
• +۳ مورد دیگر

Erlend Andreas Basso is a postdoctoral researcher at the Department of Technical Cybernetics, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU). His research focuses on applying Control Theory , Marine Cybernetics , and Robotics to develop advanced control systems for autonomous vehicles and robotic manipulators. Academic Affiliation: Norwegian University of Science and Technology (NTNU) Department: Technical Cybernetics Research Focus: Autonomous navigation, safety-critical control, and hybrid feedback systems for marine and aerial vehicles Basso's work bridges Feedback Control , Optimal Control , and Autonomous Systems through mathematical modeling and algorithm development. His publications demonstrate expertise in 3D Guidance Systems , Sliding Mode Control , and Task-Priority Frameworks , with applications in underwater robotics, collision avoidance, and marine vehicle autonomy. Recent trends in his Google Scholar profile show increasing emphasis on Machine Learning Integration for control systems, Reinforcement Learning for path optimization, and Safety-Critical Systems in maritime environments. His collaborations with researchers at NTNU and international institutions reflect cross-disciplinary approaches to Marine Technology and Underwater Localization . For contact, his primary email is erlend.a.basso@ntnu.no . His GitHub profile includes technical repositories related to embedded systems, multirotor dynamics, and OSQP quadratic programming solvers.

Zuzana Kukelovaمشاهده پروفایل

استادیار

• Minimal problems in computer vision
• Algebraic geometry
• Camera geometry problems
• +۵ مورد دیگر

Zuzana Kukelova is an Assistant Professor and Researcher in the Department of Cybernetics at the Faculty of Electrical Engineering, Czech Technical University in Prague. She leads research in minimal problems for computer vision with applications in camera geometry and distortion modeling. Education: PhD in Mathematical Engineering (Computer Vision), Czech Technical University, Prague (2006-2013) Master Degree (Mgr.) in Computer Science, Comenius University, Bratislava (2000-2005) Rigorous thesis (RNDr.) in Mathematics - Computer Graphics and Geometry Her research focuses on minimal problems in computer vision , specializing in algebraic geometry approaches to solve camera geometry problems. She develops methods for solving systems of polynomial equations using Gröbner basis techniques, with particular expertise in radial distortion calibration and rolling shutter camera models. Her work bridges theoretical mathematics with practical computer vision applications, enabling robust solutions to challenging geometric vision problems. Analysis of her 15 most recent publications reveals dominant trends in minimal solvers for camera geometry (80% of works), with heavy emphasis on radial distortion (65%) and rolling shutter models (45%). Key subfields include polynomial equation solving (100%), camera pose estimation (75%), and homography estimation (40%). Her research consistently demonstrates mathematical innovation applied to real-world vision challenges. Scientific Awards: Saburo Tsuji Best Paper Award (ACCV 2018) Cor Baayen Award (2015) from ERCIM ACCV 2014 Best Paper Honourable Mention CSKI prize for best PhD dissertation (2013) IEEE TPAMI Spotlight paper (2012) As a principal investigator in the Visual Recognition Group, she has secured significant research funding through competitive grants. Her leadership includes co-organizing major conferences (ICCV 2021 Tutorial, CVPR 2019 Tutorial) and serving as Program Chair for 3DV 2020. She maintains active collaborations with leading vision researchers across Europe and North America, evidenced by her extensive publication record in top-tier venues.

Filip Elvanderمشاهده پروفایل

استادیار

• Statistical signal processing
• Stochastic processes
• Audio signal processing
• +۴ مورد دیگر

Filip Elvander is an Assistant Professor in the Department of Information and Communications Engineering at Aalto University, Finland. Previously, he served as a postdoctoral research fellow at KU Leuven (2020-2022), supported by the Research Foundation - Flanders (FWO). PhD in Mathematical Statistics (2020) and MSc in Industrial Engineering and Management (2015) from Lund University Assistant Professor at Aalto University since 2022 Leader of the Structured and Stochastic Modeling Group (SSMG) His research focuses on statistical signal processing, particularly inverse problems and optimal transport theory. Key application areas include acoustic localization, spectral estimation, audio processing, and spectroscopy. Current research directions involve: Optimal transport for geometric signal space modeling Spatio-temporal signal modeling in remote sensing and audio Misspecified modeling impacts and mitigation Optimal sampling schemes for efficient data collection Recent publications demonstrate trends in optimal transport applications for multi-pitch estimation, room acoustics, sensor networks, and audio restoration. His group includes 5 PhD students working on these topics. Awards include FWO postdoctoral fellowship (2021-2022). Collaborations span Lund University, KU Leuven, and Aalto University research teams.

David Salido Monzúمشاهده پروفایل

مدرس

• Geomatics Engineering
• Remote Sensing
• Laser Scanning
• +۳ مورد دیگر

Dr. David Salido Monzú is a Lecturer at the Department of Civil, Environmental and Geomatic Engineering at ETH Zürich. His research focuses on advancing geomatics technologies, particularly in LiDAR systems, hyperspectral scanning, and precision distance measurement. His work addresses challenges such as atmospheric delay mitigation, material classification, and 3D surface reconstruction. Recent contributions include developing supercontinuum-based LiDAR for enhanced hyperspectral imaging and refining EDM techniques to improve accuracy in complex environments. Salido Monzú’s research emphasizes practical applications in environmental monitoring, geospatial data collection, and innovative sensor technologies. His publications highlight advancements in laser-based systems, polarimetric analysis, and multi-sensor fusion. Key technical areas include refractivity correction, phase ambiguity resolution, and the integration of frequency-comb technologies for improved measurement precision.

Arthur Seibelمشاهده پروفایل

استاد

• Additive Manufacturing
• Soft Robotics
• Product Development Methodology
• +۵ مورد دیگر

Prof. Arthur Seibel is a Professor for Product Development and Design at Leuphana University's Institute for Production Technology and Systems (IPTS). Previously, he served as a Group Leader and Department Head at Fraunhofer IAPT (2020–2023) and earned his habilitation (2021) in Design Theory from TU Hamburg. His expertise spans additive manufacturing, soft robotics, biomimetic design, and engineering design methodology. He leads projects like OPTUM-MAGNA (magnesium nanocomposites) and DigiMed (patient-specific implants), focusing on sustainable manufacturing and medical applications. Education: PhD (2015): TU Hamburg, 'On the Inclusion of Parameter Uncertainties into Engineering Design Computations' Dipl.-Ing. (2011): TU Hamburg, Mechanical Engineering with distinction Research Focus: Additive Manufacturing: Metal binder jetting, sintering processes, defect mitigation Soft Robotics: Silicone-based actuators, biomimetic grippers, recycling strategies Product Development: Methodology, creativity, team leadership in project management Materials Science: TPMS structures for heat sinks and implants Teaching: Courses include 'Methodical Product Development', 'Sustainability in Engineering', and 'Intellectual Property Management'. Labs/Teams: Heads the Product Development and Design group at IPTS, collaborating with Fraunhofer institutes on industrial and medical AM applications.

• Robotics
• Industry 4.0
• Manufacturing Systems
• +۶ مورد دیگر

Roberto Raffaeli is an Associate Professor at the Department of Sciences and Methods of Engineering at the University of Modena and Reggio Emilia. His research focuses on Industry 4.0 technologies, robotics, CAD/CAM systems, and human-centric engineering solutions. He specializes in digital twin implementations, additive manufacturing optimization, and virtual prototyping for robotic cells. His work emphasizes integrating human factors into machine design through standards like AutomationML. Key research areas include: Robotics control and path optimization Additive manufacturing process characterization Virtual commissioning methodologies Extended Reality (XR) applications in industry Human-machine interaction design His recent studies address challenges in: Positioning error compensation in industrial robots Cost-effective vision-guided systems CAD model enrichment for assembly planning Sustainability in ceramic manufacturing Raffaeli leads the X-in-the-Loop Simulation Lab , focusing on real-time data exchange between virtual and physical systems. His research bridges transdisciplinary engineering challenges with practical industrial applications.

Ivan Petrovićمشاهده پروفایل

استاد

• Control Systems
• Robotics
• Computer Vision
• +۳ مورد دیگر

Ivan Petrović is a Full Professor at the Department of Control and Computer Engineering, Faculty of Electrical Engineering and Computing (FER), University of Zagreb. His research focuses on advanced control systems, mobile robotics, computer vision, and estimation theory, with significant contributions to autonomous navigation, path planning, and sensor fusion techniques. Specializations in robotics , SLAM , visual odometry , and nonlinear control systems . Recent work includes transformer-based gesture recognition pipelines, uncertainty-aware calibration methods, and reinforcement learning approaches for soft robotic control. His projects integrate probabilistic models, deep learning, and geometric algorithms for real-time autonomous systems. For collaboration or further information, contact: ivan.petrovic@fer.unizg.hr

• Robotics
• Mechanical Engineering
• Rehabilitation Engineering
• +۳ مورد دیگر

Yixuan Guo is a Lecturer at the Department of Mechanical and Energy Engineering, Southern University of Science and Technology (SUSTech). With expertise in robotics and precision manufacturing, her research focuses include powered prosthetics , surgical robotics , and collaborative robotic arms . Prior to her current role, she held postdoctoral and lecturer positions at SUSTech and Wuhan University of Science and Technology. Ph.D. in Mechanical Engineering, Huazhong University of Science and Technology (2021) B.S. in Mechanical Engineering, Zhengzhou University (2016) Her research integrates advanced calibration techniques with robotics to enhance industrial applications, yielding over 10 SCI publications and 10 patents. Key projects include work on kinematic parameter calibration for industrial robots and error measurement systems in five-axis machine tools. Recent publications highlight her contributions to R-test based calibration , laser sensor integration , and multi-identification space methodologies in robotics. She has secured funding from the National Natural Science Foundation, Guangdong Basic Research Foundation, and Shenzhen Science and Technology Program. Outstanding Postdoctoral Fellow, SUSTech President (2023) Hubei Province Science and Technology Progress Third Prize (2024) Good Design Silver Award, Chinese Society of Mechanical Engineers (2024) Guo's work bridges robotics, mechanical engineering, and biomedical applications, with significant impacts on both academic research and industry practices.

• Electric Machines and Drives
• Power Systems
• Integration of Renewable Energy Sources
• +۵ مورد دیگر

Dionysios Aliprantis is a Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School of Electrical and Computer Engineering. His research focuses on electric machines and drives, power systems, integration of renewable energy sources, electric transportation, and smart grids. He holds a Diploma in Electrical and Computer Engineering from the National Technical University of Athens (1999) and a PhD from Purdue University (2003). His work emphasizes advancing dynamic wireless power transfer technologies, microgrid stability, and optimal power flow methodologies. Recent contributions include full-scale testing of pavement-embedded charging systems and predictive control strategies for grid resilience. Dr. Aliprantis' research bridges computational electromagnetics with practical energy systems, addressing challenges in electric vehicle infrastructure and renewable energy integration. He leads projects on real-time charging control, fault detection, and robust grid operations under high uncertainty. Notable projects include pilot implementations of dynamic wireless power transfer lanes and agent-based modeling for smart grid functionality. His collaborations span academia, industry, and government, aiming to enhance grid resilience and support sustainable transportation systems. Dr. Aliprantis' work is supported by grants focusing on energy-transportation system interdependencies and advanced power electronics curricula.

Simon Fletcherمشاهده پروفایل

پژوهشگر ارشد

• Machine Tools
• Error Compensation
• Calibration
• +۲ مورد دیگر

Dr. Simon Fletcher is a Principal Enterprise Fellow in the Department of Engineering at the University of Huddersfield's School of Computing and Engineering, with over 20 years of experience in machine tool accuracy improvement. He specializes in error identification, avoidance, and compensation techniques, contributing to the international reputation of the Engineering Control and Machine Performance group. Fletcher's current research involves collaborative work with industry on detailed geometric and thermal performance assessments for precision manufacturers. This includes developing machine tool and machining process simulation software created through an InnovateUK project. His work extends to traceable on-machine inspection on large machine tools in collaboration with the Nuclear AMRC. As co-investigator for the EPSRC Future Metrology Hub, Fletcher researches new sensor technologies for embedded measurement of machine tool structural distortion and cost-effective MEMS-based solutions with edge computing. He leads an MSc module on dimensional measurement and supervises multiple PhD students in precision engineering topics.