Data Encoding Professors and Supervisors in Netherlands | ApplySharif

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جست‌وجوی استاد و پژوهشگر

استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۱-۱۵ از ۶۵ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Raquel Fernándezمشاهده پروفایل

استاد

University of AmsterdamNetherlands

Raquel Fernández is Full Professor of Computational Linguistics and Dialogue Systems at the University of Amsterdam, where she leads the Dialogue Modelling Group at the Institute for Logic, Language & Computation (ILLC). As Vice-Director for Research at ILLC and a Fellow of the ELLIS Society, she bridges computational linguistics, cognitive science, and artificial intelligence through her research on language use in multimodal and conversational contexts. PhD in Computational Linguistics from King's College London Prior research positions at University of Potsdam and Stanford University's CSLI Her work explores how cognitive constraints, social interaction, and perception shape language use, with a focus on: Visually-grounded language processing Multimodal dialogue modeling Model uncertainty and calibration Language grounding in multimodal data Language learning and semantic change Dialogue reference resolution Recent publications analyze multimodal reasoning limitations, cross-lingual knowledge consistency, and uncertainty modeling in dialogue systems. She has received multiple accolades including an ERC Consolidator Grant , NWO VENI/VIDI/Aspasia fellowships , and EMNLP/GenBench awards . Outstanding Paper Award (EMNLP 2023) Best Data Award (GenBench Workshop 2023) ELLIS Society Fellow ERC Consolidator Grant #819455 recipient NWO VENI/VIDI/Aspasia awardee As a leader in academic service, she serves on the SIGDAT Executive Committee and chairs multiple conference committees. Her lab develops models for multimodal dialogue, visual storytelling, and grounded language understanding.

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Vedran Dunjkoمشاهده پروفایل

استاد

Leiden UniversityNetherlands

Professor Vedran Dunjko is a faculty member at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, with affiliations to the Leiden Institute of Physics (LION). He leads the Applied Quantum Algorithms group and co-founded the Quantum@LIACS initiative, focusing on the intersection of quantum computing, machine learning, and artificial intelligence. His research interests include quantum machine learning, quantum-enhanced reinforcement learning, quantum heuristics, and the application of AI to quantum computing challenges. Dunjko's work bridges theoretical foundations with experimental implementations on near-term quantum devices, exploring both quantum advantages in learning and the use of classical AI for quantum system design. The recent publications show a strong trend toward proving quantum advantages in learning tasks, optimization, and topological data analysis, with publications in Nature , Nature Communications , and NeurIPS . Key themes include quantum policy gradients, quantum TDA, and reinforcement learning for quantum circuit optimization. ERC Consolidator Grant (2024) PNAS Cozzarelli Prize (2018) Editor’s Suggestion in Physical Review Letters (2014, 2018) Featured in Physics (American Physical Society) (2014, 2018) Dunjko advises several PhD candidates and postdocs, including Rahul Bandyopadhyay, Sofiene Jerbi, and Lea Trenkwalder. He has received competitive grants, most notably the ERC Consolidator Grant in 2024. His group fosters international collaborations with institutions across Europe and industry partners. The Applied Quantum Algorithms group and the Quantum@LIACS team combine theoretical investigations with practical implementations on quantum hardware, focusing on scalable quantum algorithms and AI-driven quantum discovery.

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Antonia Krefeld-Schwalbمشاهده پروفایل

استادیار

Erasmus University RotterdamNetherlands

Antonia Krefeld-Schwalb is an Assistant Professor at the Department of Marketing Management, Rotterdam School of Management, Erasmus University. With a background in cognitive science and management, her research bridges computational modeling, eye-tracking, and consumer decision-making to address sustainability challenges. Current Affiliation: Assistant Professor, Rotterdam School of Management Research Focus: Sustainable consumer behavior, decision-making processes, and methodological improvements Key Collaborations: Columbia University, University of Geneva, Erasmus Sustainability Program Her cognitive science training informs methodological approaches like mouse/eye tracking and computational modeling applied to marketing problems. She investigates structural parameter interdependencies, external validity threats in surveys, and climate risk communication effectiveness. Recent research trends include climate adaptation strategies, sustainable behavior interventions, and meta-scientific analyses of statistical practices in consumer research. She advocates for heterogeneous population sampling and preregistration to enhance validity. Scientific Honors: Veni Grant (NWO) She develops targeted sustainability interventions through collaborations like the Erasmus Sustainability Program. Her work appears in journals such as PNAS, Journal of Marketing Research, and Psychological Review.

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Peter Zijlstraمشاهده پروفایل

استاد

Eindhoven University of TechnologyNetherlands

Peter Zijlstra is a Full Professor in the Department of Applied Physics at Eindhoven University of Technology (TU/e), leading the Molecular Plasmonics group. His research focuses on single-molecule sensing using plasmonic and nanophotonic approaches to study biomolecular interactions in complex environments. He is a core member of the Institute for Complex Molecular Systems at TU/e, collaborating across disciplines like chemistry, biomedical engineering, and mathematics. Education: MSc in Applied Physics, University of Twente (2005) PhD from Swinburne University of Technology (2009), studying plasmonic nanoparticles in optical data storage Postdoctoral fellowship at Leiden University under Prof. Michel Orrit Research Interests: Developing novel sensing concepts via nanophotonics and super-resolution microscopy. Key areas include plasmon-enhanced fluorescence, real-time biomolecular dynamics, and applications in cancer management. His work contributes to UN Sustainable Development Goals through advancements in biosensing technologies. Awards: 2013 NWO Vidi Award for research on plasmonic imaging of enzymes in living cells Teaching & Activities: Teaches courses like Advanced Optical Microscopy and Electromagnetism Supervised 32 academic works Contributed to conferences and editorial roles for journals like npj Biosensing Labs & Collaborations: Molecular Plasmonics group website: www.molecular-plasmonics.nl Marie Curie ITN SuperCol project: www.supercol.eu

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Afra Alishahiمشاهده پروفایل

استاد

Tilburg UniversityNetherlands

Afra Alishahi is a Full Professor at Tilburg University's Department of Cognitive Science and Artificial Intelligence within the Tilburg School of Humanities and Digital Sciences. Her research focuses on computational models of human language acquisition and grounded language learning, leveraging neural models to explore how language processing and acquisition occur. She has held roles including Assistant Professor at Tilburg University (since 2011) and Postdoctoral Fellow at Saarland University (2008-2011). Her work bridges computational linguistics, cognitive science, and artificial intelligence, with contributions to understanding language learning mechanisms through models that integrate visual, auditory, and linguistic data. Education: PhD (university unspecified), with prior academic roles in Iran and Germany. Awards: CoNLL 2017 Best Paper Award, 2023 Outstanding Paper Award, NWO Aspasia Grant (2015), and NWO Natural Artificial Intelligence Grant (2015). Her research has been supported by grants such as the Dutch National Research Agenda-funded project on interpreting deep learning models for text and sound. Research Interests: Grounded language learning, interaction effects in language acquisition, and neural model interpretability. Key areas include multi-modal learning (e.g., linking speech to visual scenes), computational modeling of child language learning, and probing neural networks for linguistic knowledge. She co-organized workshops like BlackboxNLP (2018-2020) and has authored over 60 publications, including influential works on phonology encoding in neural models and gender disambiguation in machine translation. Teaching: Courses include Cognitive Models of Language Learning , Computational Linguistics , and Language, Cognition & Computation . She advises master's theses and leads projects in data science and AI. Lab/Team: Leads research on computational modeling, collaboration with interdisciplinary teams (e.g., with Grzegorz Chrupała, Afsaneh Fazly), and involvement in initiatives like the Interpreting Deep Learning Models for Text and Sound project.

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Tom F.A. de Greefمشاهده پروفایل

استاد

Eindhoven University of TechnologyNetherlands

Tom F.A. de Greef is a Full Professor at Eindhoven University of Technology's Biomedical Engineering department, leading pioneering research at the intersection of synthetic biology, molecular computing, and engineered living systems. He founded the Center of Living Technologies and serves as a Core Professor at the Institute for Complex Molecular Systems (ICMS). Key research areas: Biological Computing Devices, DNA-based Data Storage, Engineered Living Materials, Synthetic Cell Engineering, Digital Chemistry, and Protocell Communication. His work has resulted in over 100 publications in Nature , Nature Chemistry , and Nature Nanotechnology , supported by prestigious awards including ERC Consolidator, Starting, and PoC grants, as well as NWO's VICI, VIDI, and VENI grants. He received the Cram-Lehn-Pedersen Prize in 2017 and was named a Groundbreaking TU/e Researcher in 2022. Leadership roles: Founding member of Center of Living Technologies, Principal Investigator at TU/e, and Fellow of the Netherlands Academy of Engineering. He supervises a large research group with >15 PhD students and leads collaborations across international institutions, focusing on programmable molecular systems and sustainable technologies aligned with UN SDGs.

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Robert Polliceمشاهده پروفایل

مدرس

University of GroningenNetherlands

Robert Pollice is a Lecturer at the Faculty of Science and Engineering , University of Groningen , specializing in Homogeneous Catalysis . His research integrates computational chemistry , machine learning , and automated experimentation to accelerate molecular design and catalyst development . Research Interests focus on homogeneous catalysis , quantum chemistry , and machine learning applications. His work addresses challenges in reaction mechanism modeling , noncovalent interactions , and inverse molecular design , leveraging closed-loop optimization and large language models for chemical data analysis . Publications span quantum chemical simulations , solvation energy calculations , excited state engineering , and automated catalyst discovery . His recent studies explore inverted singlet-triplet gaps , machine learning for reaction modeling , and SELFIES for molecular string representations . Peer-review Contributions include evaluations for journals like Organic Process Research & Development , Materials Advances , and Chem , reflecting his expertise in catalysis , quantum chemistry , and AI-driven chemical discovery .

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Arno Siebesمشاهده پروفایل

استاد

Utrecht UniversityNetherlands

Arno Siebes is Professor of Algorithmic Data Analysis in the Department of Information and Computing Sciences at Utrecht University's Faculty of Science. His research focuses on data mining methodologies, particularly pattern mining and Minimum Description Length (MDL) principles. Key research areas include: Developing efficient algorithms for pattern discovery Applying MDL to data characterization Creating interpretable models for complex datasets Addressing challenges in data science education Recent publications demonstrate applications in diverse domains including mobility analysis, genomic screening, and pandemic response. His work combines theoretical foundations with practical implementations for knowledge discovery.

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Gesa van den Broekمشاهده پروفایل

استادیار

Utrecht UniversityNetherlands

Gesa van den Broek is an Assistant Professor at Utrecht University's Faculty of Social and Behavioural Sciences , specializing in Education and Learning: Development in Interaction . Her research focuses on instructional design, cognitive psychology, and memory studies, particularly in foreign language pedagogy and higher education research. Areas of Expertise: Instructional Design, Cognitive Psychology, Memory Studies, Foreign Language Pedagogy Research Themes: Dynamics of Youth (DoY), Game Research, Higher Education Research Her recent work investigates retrieval practice mechanisms, stepwise worked examples, and multimedia learning effects. She actively collaborates on educational technology tools like ET_cam_home and participates in public engagement activities, including media contributions to Dutch outlets like Volkskrant and Trouw.

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Arianna Bisazzaمشاهده پروفایل

دانشیار

University of GroningenNetherlands

Arianna Bisazza is an Associate Professor in the Computational Linguistics Group at the University of Groningen, where she leads the InClow research group focused on Interpretable, Cognitively inspired, Low-resource language models. Her work bridges computational linguistics, cognitive science, and language acquisition to develop more robust and interpretable language processing algorithms that can adapt to diverse linguistic phenomena worldwide. Dr. Bisazza's research interests span statistical modeling of human languages in multilingual contexts, with particular focus on improving language model performance for "challenging" or low-resource languages. Her work explores how insights from human language acquisition can inform better language modeling techniques, and she investigates methods to make state-of-the-art NLP systems more interpretable and transparent. As a cross-disciplinary researcher, she actively seeks to enhance our understanding of human language processing and evolution through computational modeling tools. Her recent publications reveal a strong emphasis on multilingual evaluation frameworks (like TurBLiMP and MultiBLiMP), interpretability of language models, and connections between human language acquisition and neural network learning. Her work consistently addresses the challenge of making language technology more robust across diverse linguistic structures and typological features. Outstanding Paper Award at the BabyLM Challenge (CoNLL'24 Shared Task) for "BabyLM Challenge: Exploring the Effect of Variation Sets on Language Model Training Efficiency" Dr. Bisazza currently leads a Vidi project funded by the Dutch Research Council (NWO) on improving low-resource language modeling through child language acquisition insights. She is also part of two national consortium projects funded by NWA-ORC initiatives: InDeep (Interpreting deep learning models for language, speech & music) and LESSEN (Low Resource Chat-based Conversational Intelligence). She supervises multiple PhD students, including two China Scholarship Council (CSC)-funded researchers working on simulating human patterns of language learning and change. Her earlier research was supported by a Veni grant (2017-2021) focused on understanding and improving the encoding of linguistic structure in Neural Machine Translation models. As head of the InClow research group, Dr. Bisazza oversees a team investigating interpretable, cognitively inspired approaches to low-resource language modeling. The group's work combines insights from cognitive science and linguistics with cutting-edge NLP techniques to develop language models that better reflect human language processing capabilities, particularly in resource-constrained settings.

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Utku Yavuzمشاهده پروفایل

استادیار

University of TwenteNetherlands

Dr. Utku Yavuz is an Assistant Professor in the Biomedical Signals and Systems Department at the TechMed Centre. His expertise spans neuromuscular physiology, wearable sensor technologies, and clinical monitoring systems. He holds a PhD in Biomedical Engineering from Ege University, complemented by earlier degrees in Physics Engineering (Hacettepe University) and Biophysics (Hacettepe University). Research Focus: Motor unit physiology, neuromuscular modeling, and clinical applications of wearable sensors. Key Areas: Spinal motor neuron behavior, electromyography (EMG), and translational technologies for diabetes and musculoskeletal health. Dr. Yavuz’s work bridges neuroscience and engineering, addressing challenges in prosthetic design, real-time neuromuscular signal decoding, and improving clinical decision-making through sensor data analysis. Recent studies include optimizing wearable glucose monitors and analyzing muscle-tendon dynamics in amputees. His research outputs span 43 publications, with contributions to high-impact journals like BMC Digital Health and IEEE Sensors Journal . Collaborations include institutions focused on biomechanics, robotics, and clinical informatics. Advising: Supervised 1 graduate project, though specific advisee names are not listed. Active in academic activities such as thesis examinations and conference presentations.

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Jack Yangمشاهده پروفایل

استادیار

Wageningen University & ResearchNetherlands

Jack Yang is an Assistant Professor in the Department of Food Science at Wageningen University & Research, specializing in the Physics and Physical Chemistry of Foods. His research focuses on interfacial phenomena in food systems, particularly plant protein functionalization, oleosome membrane mechanics, and foam/emulsion stabilization. He leads multiple interdisciplinary projects combining physics, data science, and food technology. Key research interests include: Interfacial rheology of plant proteins and phospholipids Stabilization mechanisms in food foams and emulsions Utilization of plant-based ingredients (rapeseed, mung bean, asparagus) Development of sustainable food processing methods Recent work emphasizes protein-phenolic interactions, AI-driven food system modeling, and valorization of agricultural by-products. He collaborates extensively with industry partners on plant protein innovation and has pioneered methods for high-yield oleosin extraction. Current projects include large language model applications in food product development and physics-encoded neural networks for prediction of complex food systems. As a co-promotor for seven PhD candidates, Yang supervises research on topics like functional protein-extract interactions, microstructure imaging of protein interfaces, and combining physics with data science for functionality prediction. His work bridges fundamental colloid science with practical food formulation challenges.

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Martin A. Vinckمشاهده پروفایل

استاد

Radboud UniversityNetherlands

Prof. Dr. Martin Vinck is a Professor at the Donders Centre for Neuroscience in the Department of Neurophysics , Radboud University. He is also a Research Group Leader at the Ernst Strüngmann Institute for Neuroscience and a faculty member at the International Max Planck Research School for Neural Circuits . Current affiliations: Radboud University, ESI Frankfurt, Max Planck Research School Positions: Professor, Research Group Leader, Review Editor for eLife Research Interests span computational and systems neuroscience, focusing on predictive processing in biological and artificial neural networks. His lab investigates: How distinct excitatory/inhibitory neurons regulate plasticity and flexible information processing Neural ensemble coding via spatio-temporal patterns and spike sequences Self-supervised learning mechanisms using spatial/temporal predictions Gamma oscillations, synchronization, and their role in visual encoding Scientific Contributions include developing algorithms for high-dimensional neural data analysis, co-supervising key studies on interneuron dynamics, and pioneering work on predictive coding in V1. His grants include the ERC Starting Grant (2019) and VIDI Grant (2024) . Lab Members include PhD students Jinke Liu, Jahan Esfandiarii, Athanasia Tzanou and alumni Irene Onorato, Marius Schneider, Ana Clara Silveira Broggini . He actively promotes open science , develops the Spike Toolbox , and reviews for journals like Neuron, eLife, PLoS Computational Biology .

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Daniel Braunمشاهده پروفایل

پژوهشگر

University of TwenteNetherlands

Daniel Braun is a Researcher at the Digital Society Institute , affiliated with Technical University of Munich . His work bridges Artificial Intelligence , Natural Language Processing , and LegalTech , focusing on automated legal assessment of contracts, ethical dimensions of AI, and consumer protection in the digital era. Education: Bachelor in Artificial Intelligence at Saarland University PhD in Automated Semantic Analysis, Legal Assessment, and Summarization of Standard Form Contracts at Technical University of Munich Master in Creating Textual Driver Feedback from Telemetric Data at University of Aberdeen Research Interests revolve around applying NLP to legal and engineering domains. Key areas include machine learning for contract analysis , ethical AI frameworks , and consumer protection through automated systems . His recent work explores adversarial attacks on text detectors, lexical alignment in chatbots, and robustness in generative AI detection. Publications (2024-2025) highlight advancements in German consumer contract analysis , disagreement handling in legal datasets , and regulatory debates around AI . Notable outputs include the AGB-DE corpus and studies on black-box neural text detectors . Technical Expertise spans data mining , process mining , and domain-specific NLP . He has contributed to smart contract analysis , conversational AI , and language models for engineering and legal contexts .

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Hugo Ledouxمشاهده پروفایل

پژوهشگر

Delft University of TechnologyNetherlands

Hugo Ledoux is an academic affiliated with the Faculty of Architecture and the Built Environment at Delft University of Technology, specializing in Urban Data Science. His research focuses on 3D geospatial modeling, including CityGML standards, terrain analysis, and automated reconstruction of urban structures. He has contributed to projects like the DeltaDTM coastal terrain model and the cjdb database solution for CityGML. Education: Not explicitly detailed in text, but inferred through academic roles and publications. Research interests emphasize 3D geoinformation systems, remote sensing applications, and urban data science. His work addresses challenges in 3D city models, building reconstruction, and geospatial validation tools like Val3dity. Recent efforts include improving global terrain models using ICESat-2 and GEDI lidar data. Publications span automated building reconstruction workflows, terrain accuracy assessments, and semantic-guided facade modeling. Awards include the Best Presentation at 3DGeoInfo 2020 and the U.V. Helava Award for Best Paper in 2011. Ledoux has supervised 4 academic works and actively participates in conferences, editorial activities, and open-source software development for geospatial applications. Labs/Teams: Involved in TU Delft’s 3D geoinformation research, contributing to tools like 3dfier and CityJSON for 3D data interoperability.

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