جست‌وجوی استاد و پژوهشگر

• Solar Energy Optimization
• Control Systems
• Remote Laboratories
• +۷ مورد دیگر

Fariba Moghaddam is an Ordinary Professor at HES-SO Valais-Wallis - Haute Ecole d'Ingénierie, leading the Power & Control orientation under the Institut Systèmes industriels. She specializes in renewable energy systems, control engineering, and educational technology, with a focus on solar energy optimization, remote laboratories, and sustainable development initiatives in the Global South. Her work spans academic leadership, research, and pedagogical innovation. Education: PhD in Control Engineering from École Polytechnique Fédérale de Lausanne (EPFL), 1996. Additional academic background in electrical and mechanical engineering. Research Interests: Solar tracking systems, photovoltaic-thermal (PVT) integration, machine learning applications in energy systems, remote experimentation platforms, and collaborative educational infrastructure. Recent projects include the Solar Energy Optimization via Remote Platforms and DEAR MENA (Digital Education & Research for MENA). Grants & Projects: Solar Energy Optimization (2022-2026): Swiss National Science Foundation-funded project developing cost-effective sun tracking solutions. DEAR MENA (2019-2024): Cluster fostering digital education partnerships between Swiss and MENA institutions. Sustainable Laboratories (2020-2021): Platform for redistributing lab equipment to emerging economies. MOOLs (2018-2021): Massive Open Online Laboratories for global engineering education. Labs & Infrastructure: Spearheaded the Remote Real-Time Research Platform for control engineering, enabling global access to lab equipment via secure web interfaces. Collaborates on IoT-integrated systems for remote experimentation and industrial automation. Awards: None explicitly listed, but recognized for contributions to sustainable energy and educational equity through numerous funded projects.

• Construction Project Management
• Building Information Modeling (BIM)
• Geographic Information Systems (GIS)
• +۴ مورد دیگر

Domer Bernd is an Associate Professor at the Geneva School of Landscape, Engineering and Architecture (HES-SO), part of the University of Applied Sciences and Arts Western Switzerland. He serves as Head of the MIC Group, Member of the Bauen digital Switzerland committee, Member of the EG-ICE committee, and Co-director of the CAS for BIM coordination. His work focuses on advancing digital technologies in the Architecture, Engineering, and Construction (AEC) industry. Professor Bernd holds a BA HES-SO in Architecture from the Geneva School of Landscape, Engineering and Architecture, a BSc in Civil Engineering from EPFL, a BSc HES-SO in Civil Engineering from the Geneva School, and an MSc HES-SO in Engineering. His academic background spans both theoretical and practical aspects of construction engineering and digital technologies. Domer Bernd's research interests center around digital transformation in construction, with particular expertise in Building Information Modeling (BIM), Geographic Information Systems (GIS), digital modeling of built assets, and the application of Artificial Intelligence in the AEC industry. His work addresses critical challenges in construction project management, infrastructure digitalization, and subsurface modeling. His extensive publication record demonstrates a strong focus on BIM-GIS interoperability, digital workflows for infrastructure projects, and subsurface modeling. Recent work shows increasing emphasis on automation of digital processes, integration of AI in construction, and practical implementation of BIM standards in real-world projects, particularly for infrastructure and underground applications. Professor Bernd leads multiple research projects including CU_OFROU_PAB (funded by OFROU), SousEtoile (funded by the State of Geneva), Flash Learn BIM (funded by HES-SO), and several completed projects such as BridgeTwin, digiMabs, and InnoSubsurface. These projects collectively represent over 1.6 million CHF in research funding, demonstrating significant institutional support for his work. As Head of the MIC Group, Bernd oversees research activities focused on digital construction technologies, BIM implementation, and infrastructure modeling. The group collaborates with academic institutions across Switzerland and professional partners in the construction industry, creating a robust ecosystem for applied research in digital construction.

• Fluid Mechanics
• Microfluidics
• Inkjet Technology
• +۴ مورد دیگر

Balestra Gioele is a Full Professor at the Fribourg School of Engineering and Architecture (HES-SO) and Co-Head of the iPrint Institute. His primary affiliations include the BSc HES-SO in Mechanical Engineering, where he teaches CFD simulation and Fluid Mechanics. He leads the ComplexFluidPrint project (since 2019), developing advanced inkjet technologies for complex fluids. His research focuses on fluid mechanics, microfluidics, and rheology, with recent projects addressing micro-manipulation via liquid plugs and composite materials for IoT applications. Research interests include thin film instabilities, droplet dynamics, and industrial applications of inkjet technology. Notable projects include the experimental validation of liquid plug-based micro-manipulation (2020-2022), funded by HEIA-FR, and a collaborative effort on multifunctional composite materials with COMATEC (2019-2021), yielding 250'000 CHF in funding. His work bridges theoretical models, numerical simulations, and experimental validation. Publications span topics like machine learning for inkjet behavior prediction (2023), karst drapery morphogenesis (2021), and instability mechanisms in curved substrates (2018-2020). Current research emphasizes inertial microfluidics, airflow visualization for inkjet systems, and advanced nozzle plate manufacturing (2024). Grants: HES-SO, HEIA-FR, COMATEC Team Collaborations: Over 20 researchers across projects, including Domae Yoshinori, Maturo Jonas, and Gallaire François Labs/Teams: iPrint Institute, ComplexFluidPrint team

Jeanine Reutemannمشاهده پروفایل

مدرس

• Science Communication
• Educational Media
• Digital Education
• +۳ مورد دیگر

Dr. Jeanine Reutemann serves as a Lecturer at the Department of Environmental Systems Science, ETH Zurich, where she leads the Media & Methods Lab within the Unit for Teaching and Learning (UTL). Her role encompasses producing co-designed audiovisual media for ETH lecturers, advancing digital media competencies across the institution, and exploring innovative media technologies for future teaching through flagship projects. Her research centers on the science-society interface , specializing in translating complex scientific concepts through audiovisual media. Key interests include the epistemological potential of visual language in education, sustainable production of science videos, mixed reality applications, and combating misinformation. She investigates how media design shapes public understanding of science and enhances learning experiences in higher education, with particular focus on deepfakes, truth verification, and co-design methodologies. Analysis of her publication trajectory (2016-2024) reveals an evolution from foundational studies on MOOC video styles to current work on sustainable educational media and mixed reality. Her research consistently bridges theory and practice, emphasizing ethical considerations, societal impact, and the transformative role of audiovisual media in science communication. Recent publications highlight growing attention to environmental sustainability in media production and AI's implications for scientific content generation. Dr. Reutemann actively contributes to interdisciplinary projects funded by the Swiss National Science Foundation (SNF), including the r4d programme. She has collaborated with Oxford University, WSL, WWF/COOP, and others to produce science documentaries addressing global challenges such as inequality, sustainability, and peacebuilding, including the full-length documentary 'Inequality and Conflict - Beyond us and Them'. She heads the Media & Methods Lab, a dynamic team that partners with ETH lecturers to co-create educational media, designs continuous professional development programs in digital media competencies, and pioneers new media technologies through projects like the Science Filmmaking Hackathon (producing AI-themed animations) and mixed reality character prototypes for campus navigation. Current initiatives include developing audiovisual essays with the gta archive and science communication videos leveraging internet meme culture for environmental science students.

• Fluid Mechanics
• Hydraulic Engineering
• Turbomachinery
• +۳ مورد دیگر

Prof. François Avellan is a prominent academic at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences and the Department of Mechanical Engineering. His research focuses on hydraulic machinery, particularly Francis turbines, Pelton turbines, and cavitation phenomena. Key areas include CFD simulations, turbine design optimization, and hydropower system analysis. He leads projects like DuoTurbo (counter-rotating hydroturbines for energy recovery) and investigates fluid-structure interactions in off-design turbine operations. His work bridges experimental fluid mechanics with computational modeling, addressing challenges in renewable energy and grid stability. Education details are not explicitly provided in the text, but his extensive scholarly output indicates a strong academic background in mechanical engineering and fluid dynamics. Collaborations with institutions like the Laboratory of Hydraulic Machines (LMH) and the Swiss Federal Institute of Technology underscore his interdisciplinary involvement. Research interests emphasize cavitation dynamics, turbine instability mechanisms, and hydroacoustic resonance prevention. Recent studies explore part-load resonance risks, vortex rope behavior, and the integration of emerging hydropower technologies. His contributions span both fundamental and applied research, impacting turbine efficiency, energy recovery systems, and sustainable energy solutions. Publications highlight advancements in CFD validation, particle-based methods for erosion prediction, and predictive control of unstable flows. Innovations like the Y-junction hydraulic short-circuit and variable-speed pump-turbine simulations demonstrate practical applications of his research. Ongoing work includes multiscale erosion modeling and strategic hydropower potential assessments for Switzerland. Laboratory affiliations include the Laboratory of Hydraulic Machines (LMH), where experimental facilities support his investigations into turbine dynamics and fluid mechanics. His research directly informs industrial practices in hydropower plant design and operational reliability.

Konrad Tiefenbacherمشاهده پروفایل

استادیار

• Climate Science
• Remote Sensing
• Artificial Intelligence
• +۵ مورد دیگر

Konrad Tiefenbacher is a dual tenure-track Assistant Professor at the University of Basel and ETH Zürich, holding positions in the Department of Chemistry (Basel) and the Department of Biosystems Science and Engineering (ETH Zürich). He specializes in applying artificial intelligence and remote sensing to study glaciology, climate science, and environmental systems. His research bridges molecular synthesis (from his PhD in natural product chemistry) with cutting-edge AI-driven Earth observation. Education: Chemical studies at the Technical University of Vienna and University of Texas at Austin, followed by a PhD under Prof. Mulzer (University of Vienna) focusing on total synthesis of bioactive natural products. Postdoctoral research in molecular recognition at The Scripps Research Institute (Prof. Rebek). Became an independent researcher at TU Munich in 2012 before his current dual appointment since 2016. Research interests include deep learning for glacier calving front detection, retrogressive thaw slump mapping, GNSS reflectometry, and self-supervised environmental monitoring. His work integrates multi-sensor data and transformer networks for climate foundation models, with projects like AI-CORE addressing Arctic and Antarctic cryosphere challenges. Publications emphasize AI applications in polar regions, such as calving front dynamics in Greenland/Svalbard, permafrost disturbances, and methane detection. His methods combine semantic segmentation (e.g., PixelDINO) and transformer architectures (DDM-Former) for high-resolution Earth surface analysis. Labs/Teams: Leads the Synthesis of Functional Modules group, collaborating on AI-driven environmental monitoring tools and large-scale datasets like DARTS and IceLines.

• Seismic Hazard Assessment
• Ground Motion Modeling
• Site Effects Analysis
• +۶ مورد دیگر

Dr. Afifa Imtiaz is a scientific researcher at the Swiss Seismological Service (SED), ETH Zurich, since 2019. Her work focuses on earthquake hazard and risk assessment, particularly in Basel, Switzerland. She specializes in seismic ground motion analysis, site effects, and microzonation studies. Dr. Imtiaz holds a PhD in Engineering Seismology from Grenoble Alpes University (2015) and has extensive postdoctoral experience in France and Switzerland. Education: PhD in Engineering Seismology (2015), Grenoble Alpes University MSc in Engineering Seismology (2011), Grenoble Alpes University Research Interests: Dr. Imtiaz investigates spatial variability of ground motion in active seismic regions, focusing on basin effects and near-source dynamics. She develops numerical models to predict amplification and coherence patterns, with applications to urban risk assessment. Her work integrates geophysical data (e.g., shear-wave velocity profiles) with probabilistic risk frameworks. Key Projects: ARES PRD: Earthquake risk reduction in Haiti ANR EXAMIN: Ground motion variability for industrial infrastructure IMAGE: Geothermal exploration in sedimentary basins Basel Urban Seismic Risk Model: 3D geological-seismological integration Scientific Contributions: Her research bridges seismic hazard modeling with practical risk mitigation, particularly in urban environments. She has pioneered methods for combining ambient noise data with morphometric analyses to map resonance effects. Recent work focuses on scenario-based loss assessments and probabilistic amplification mapping.

Zhengqing Wuمشاهده پروفایل

پژوهشگر

• Machine Learning
• Robotics
• Neural Networks
• +۳ مورد دیگر

Zhengqing Wu is a Doctoral Assistant and Student at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Laboratory for Information and Inference Systems (LIONS) under the Institute of Electrical Engineering (IEM) within the School of Engineering . Their research focuses on machine learning, robotics, quantum computing, and control systems, with notable contributions to neural network optimization, terrain-aware robotics, and quantum parameter-efficient fine-tuning. They are part of the Doctoral Program in Computer and Communication Sciences (EDIC). Key research interests include layer-wise quantization in neural networks, analysis of shallow ReLU networks' loss landscapes, and developing terrain-aware control systems for quadrupedal robots. Their work bridges theoretical insights (e.g., neural noise dynamics) with applied robotics and quantum computing advancements. Education: Doctoral Program in Computer and Communication Sciences (EDIC), EPFL Affiliations: LIONS Lab, EPFL No scientific awards are explicitly mentioned in the provided texts. Their advising and grant activities are not detailed here. The LIONS Lab focuses on information theory, signal processing, and machine learning applications.

Laurence Mylène Brandenbergerمشاهده پروفایل

پژوهشگر

• Legislative Behavior
• Parliamentary Networks
• Social Influence in Politics
• +۵ مورد دیگر

Laurence Mylène Brandenberger is a Postdoctoral Researcher at the Department of Political Science, University of Zurich, specializing in legislative behavior and parliamentary networks. Their research examines the intersection of social relations and political decision-making, with a focus on systemic spill-over effects in policy networks and enhancing democratic transparency through projects like the DemocraSci Knowledge Graph. Current focus on temporal reciprocity in congressional collaborations Developing computational tools (embed2discover, Data2neo) for policy network analysis Recipient of SNF and SDSC grants for political performance and advocacy studies Teaching course: The Politics of Influence: Interest Groups, Lobbying, and Political Advocacy (2025) Their work bridges network science and political decision-making, utilizing large-scale datasets spanning decades and multiple policy sectors. Laurence applies causal network analysis to unravel dependencies in political systems and advocates for citizen-oriented platforms to reverse electoral decline and enhance civic engagement. Prior research includes quantifying triadic closure in political networks, studying cross-continental policy networks, and analyzing conflict event data through relational event models. Their methodological contributions in dictionary-based content analysis and multi-edge network modeling have advanced empirical approaches in political science. SNF Grant (2024): Measuring Political Performance of Swiss MPs SDSC Grant (2025): Studying Political Advocacy As part of the Institute of Political Science at the Faculty of Philosophy, Laurence actively contributes to computational infrastructure development and open science initiatives aimed at improving policy transparency and reducing polarization in Western democracies.

Robert Weibelمشاهده پروفایل

استاد

• Geographic Information Science
• Cartographic Generalization
• Movement Analysis
• +۱۰ مورد دیگر

Robert Weibel is a Professor in the Department of Geography at the University of Zurich, where he conducts cutting-edge research in Geographic Information Science (GIScience), cartographic generalization, movement analysis, and human mobility. His work bridges computer science, geography, and health sciences, with strong applications in aging, environmental monitoring, and urban planning. Research Interests: His primary research areas include: Computational movement analysis and trajectory modeling Cartographic and map generalization using AI and deep learning Indoor and outdoor navigation systems Spatial cognition and human mobility GIScience and geospatial data science Linguistic geography and dialect modeling Publication Trends: His recent publications (2023–2025) reflect a strong focus on AI-driven map generalization (e.g., SpaGAN, GNNs), explainable AI in cartography, indoor navigation modeling, and interdisciplinary studies on human mobility in aging and post-stroke recovery. He frequently publishes in top journals such as the International Journal of Geographical Information Science , ISPRS Journal , and Computers, Environment and Urban Systems . Scientific Contributions: Robert Weibel is a leading figure in GIScience, particularly in map generalization and movement data science. He has contributed to major research initiatives including the MOBITEC-Stroke project and has co-organized key conferences like GIScience. His work on real-time generalization, semantic modeling, and deep learning for cartography has advanced the field significantly. Advising and Collaboration: He has supervised numerous students and collaborators across disciplines, including in gerontology, computer science, and ecology. His research often involves interdisciplinary teams and international partnerships. Labs and Teams: He is affiliated with the GIScience research group at the University of Zurich, contributing to projects involving mobile mapping, real-time data processing, and spatial analytics. His work is integral to the development of intelligent geospatial systems and e-learning in GIS (e.g., the GITTA project).

• Applied Microeconomics
• Microeconometrics
• Labor Economics
• +۸ مورد دیگر

Mattia Albertini is a Post-Doctoral Researcher at the Institute for Economic Research (IRE) within the Faculty of Economics at Università della Svizzera italiana (USI). He previously completed his Ph.D. at the Institute of Economics (IdEP), USI, and participated in the Swiss Program for Beginning Doctoral Students in Economics. He holds a Master of Science in Economics with a minor in Data Science from USI and a Bachelor in Economics from the University of Pavia. Bachelor in Economics, University of Pavia Master of Science in Economics (minor in Data Science), Università della Svizzera italiana Ph.D. in Economics, Università della Svizzera italiana Swiss Program for Beginning Doctoral Students in Economics, Swiss National Bank His research focuses on applied microeconometrics with applications in health, labor, public, urban, and environmental economics. A recurring theme in his work is the spatial dimension of economic outcomes, including residential integration, real estate market dynamics, and geographic proximity to borders or hazards. He integrates data science techniques, particularly programming in Python and STATA, to develop tools for empirical analysis and reproducible research. The collection of articles reflects a strong trend in applied econometrics, causal inference, and spatial analysis. His work spans health economics (e.g., benzodiazepine prescribing), labor and urban economics (e.g., migration, job accessibility), and environmental and insurance economics (e.g., natural hazard impacts). Methodologically, he engages with difference-in-differences, event study designs, spatial modeling, and text analysis, often building or applying computational tools to address research questions. He has contributed to teaching at both the Bachelor's and Master's levels, particularly in microeconomics, macroeconomics, and data-intensive courses such as Textual Analysis and Spatial Data for Economists. Teaching Assistant, Microeconomics A (Bachelor) Teaching Assistant, Macroeconomics B (Bachelor) Teaching Assistant, Textual Analysis and Spatial Data for Economists (Master) Mattia Albertini is actively involved in open science, sharing code for econometric methods, data visualization, and application development (e.g., a gluten-checking app) on GitHub. His interdisciplinary approach combines economic theory, statistical modeling, and computational tools to investigate real-world policy-relevant questions.

Matija Piškorecمشاهده پروفایل

پژوهشگر

• Machine Learning
• Complex Systems
• Statistical Inference
• +۵ مورد دیگر

Matija Piškorec is a Senior Research Associate at the Faculty of Informatics, University of Zurich. His research spans machine learning, complex systems, and blockchain technologies, with a focus on statistical inference of social influence and network analysis. Primary Affiliation: Faculty of Informatics, University of Zurich Research Interests Machine learning and complex systems Statistical inference of influence in online social networks Blockchain technologies and distributed ledger systems Information visualization and interactive web applications in computational biology Publications His recent work explores blockchain networks like Polkadot and Ethereum, analyzing their structure and consensus mechanisms. Earlier research focuses on social network influence, financial data cohesiveness, and computational biology tools. Awards No scientific awards or honors were explicitly mentioned in the text. Additional Contributions Developed web-based visualization tools (e.g., MultiNets) and applied machine learning to diverse domains, including microbiology and finance.

Chat Wacharamanothamمشاهده پروفایل

پژوهشگر

• Human-Computer Interaction
• Interactive Statistics
• Scientific Decision-Making
• +۷ مورد دیگر

Chat Wacharamanotham is an external lecturer and researcher at Swansea University's Computational Foundry, having joined from the University of Zurich (UZH) in July 2022. Prior to UZH, he was affiliated with RWTH Aachen University in Germany. His work bridges interactive technologies and scientific decision-making, particularly in the context of statistics. His research interests lie at the intersection of Human-Computer Interaction, Interactive Statistics, and Scientific Decision-Making . He investigates how interactive tools can support scientists in planning experiments and interpreting statistical reports, using methodologies such as Human-Centered Design, Contextual Inquiry, and lab experiments with eye- and motion-tracking. His work emphasizes transparency, usability, and cognitive support in data analysis workflows. The recent publications highlight a consistent focus on interactive statistical tools, experimental design support, and gaze-based analysis . Projects like Argus, Touchstone2, and Statsplorer demonstrate a trajectory toward building interactive systems that guide users through complex statistical reasoning. The research integrates visualization, user interface design, and empirical evaluation, primarily within the HCI domain. While no scientific awards are listed in the provided text, his contributions to the field are evident through his active publication record in premier venues like CHI and IEEE TVCG. Chat Wacharamanotham is involved in teaching, including a seminar on Experiments in Human-Computer Interaction for Master's students. Although he is no longer available for thesis supervision, he continues to contribute to academic instruction at UZH. His research is conducted within a collaborative lab environment, as evidenced by co-authored works with teams from various institutions.

Gaétan Raynaudمشاهده پروفایل

پژوهشگر

• Fluid-structure interaction
• Fluid Dynamics
• Mechanical Engineering
• +۳ مورد دیگر

Gaétan Raynaud is a Researcher and Doctoral Assistant at the École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the Unsteady Flow Diagnostics Laboratory (UNFOLD) within the Institute of Mechanical Engineering (IGM) under the School of Engineering (STI) . His research focuses on fluid-structure interaction, fluid dynamics, and robotics, with applications in areas such as soft robotics, material design, and real-time sensing systems. He contributes to projects like the development of highly agile flat swimming robots and studies on flapping flag dynamics. His work integrates experimental fluid mechanics, computational modeling, and machine learning approaches, such as Physics-Informed Neural Networks (PINNs), to solve complex fluid-structure interaction problems and optimize robotic systems. Labs/Teams: UNFOLD Lab (EPFL), collaborating on interdisciplinary projects involving robotics, fluid dynamics, and soft materials. Technical Expertise: Event-based sensing, soft robotics actuation, data-driven modeling, and computational fluid dynamics.

Luciano Andres Abriataمشاهده پروفایل

پژوهشگر

• Protein NMR
• NMR of paramagnetic systems
• Metalloproteins
• +۴ مورد دیگر

Luciano Andres Abriata is a Researcher and Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with multiple departments including the School of Engineering (SV), the Protein Production and Structure Core Facility (PTSPS), and educational units like EDCH and EDMS. His primary role is within the UPDALPE group under Prof. Dal Peraro, focusing on structural biology and molecular modeling. Research Interests: Protein NMR and paramagnetic systems analysis Structural studies of metalloproteins and biomolecular spectroscopy Development of web-based tools for molecular modeling (e.g., HandMol, MoleculARweb) AI-driven approaches in structural biology and protein engineering Teaching & Courses: Integrative Structural Biology for Life Sciences Recombinant Protein Expression in Animal Cells Bio-NMR Techniques (Levels 1 & 2) Labs & Collaborations: Active in the Laboratory of Bioengineering and Microtechnology (LBM) and involved in interdisciplinary projects blending computational methods with experimental structural biology. His work emphasizes translating molecular insights into educational tools and sustainable biocatalysis applications.