جستجوی استاد راهنمای دانشگاهی

Dr. Guillermo Amador is an Assistant Professor in the Experimental Zoology department at Wageningen University & Research. His research focuses on understanding how animals interact with complex environments through locomotion, adhesion, and fluid dynamics. He investigates biological systems like insects, plants, and marine organisms to inspire bio-engineered solutions for robotics, microfluidics, and material science. Amador received his PhD in Mechanical Engineering from Georgia Institute of Technology (USA), followed by postdoctoral research at the Max Planck Institute for Intelligent Systems (Germany) and a Marie Sklodowska-Curie fellowship at TU Delft (Netherlands). His expertise spans biophysics, biomaterials, and biomechanics, with a focus on self-cleaning mechanisms and bioadhesion. He collaborates with the 4TU consortium on Dutch Soft Robotics to develop bio-inspired designs. His work bridges fundamental biology with engineering applications, emphasizing interdisciplinary approaches to solve challenges in robotics and environmental science. Amador teaches courses including Biomimetics and Functional Zoology , integrating his research into education. His research highlights include studies on cuttlefish suction cups, stick insect adhesion, and pollen transport mechanisms in pollinators.

• Biophysics
• Biomaterials
• Biomechanics
• +۷ مورد دیگر

Karsten Lambers is Professor of Digital and Computational Archaeology at the Faculty of Archaeology, Leiden University, where he leads research and teaching in the application of computational methods to archaeological data. His work integrates machine learning, remote sensing, text mining, and citizen science to advance archaeological prospection and heritage management. He is affiliated with the Department of Archaeological Sciences and plays key roles in research groups and university-wide initiatives such as SAILS and ARCHON. His research interests span Digital Archaeology , Machine Learning in Archaeology , Remote Sensing , Geoarchaeology , and Human-Environment Interaction . He investigates how computational tools can extract meaningful archaeological information from large datasets, including LiDAR imagery and excavation reports. His fieldwork spans Central Europe and Latin America, with a focus on prehistoric landscapes and cultural heritage. The analysis of his recent publications reveals a strong trend toward automated detection using deep learning (e.g., R-CNN, WODAN), named entity recognition in archaeological texts (e.g., ArcheoBERTje), and citizen science integration for data validation. His work bridges archaeology with computer science, geomatics, and environmental science, emphasizing interdisciplinary collaboration and methodological rigor. His scientific awards include: Best Thesis Award (University of Zurich, 2005) EUROPA NOSTRA Award (2020, 2022) Membership in the German Archaeological Institute (since 2022) Lambers actively supervises students and leads major research projects such as ABMA, EXALT, and Heritage Quest. He has secured substantial research funding and collaborates widely with computer scientists, geophysicists, and palaeoecologists. His teaching includes digital methods, modeling, and simulation, often linked to ongoing research. He has also contributed to open educational resources and digital textbooks in archaeology. He leads or participates in several research labs and teams, including the Digital Archaeology Research Group (which he chairs), the Heritage Quest citizen science project, and interdisciplinary teams focusing on alpine terraces and Iraqi prospection. His work emphasizes the integration of digital tools into practical archaeological workflows, advocating for complementary human-computer strategies.

• Digital Archaeology
• Computational Archaeology
• Machine Learning in Archaeology
• +۱۳ مورد دیگر

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.

• Computational Linguistics
• Dialogue Systems
• Cognitive Science
• +۳ مورد دیگر

Mohammad Aliannejadi is an Assistant Professor at the IRLab (formerly ILPS) within the Informatics Institute at the University of Amsterdam. His research focuses on Information Retrieval (IR), machine learning, natural language processing (NLP), and conversational systems, particularly in modeling user information needs on mobile devices and conversational search systems. He holds a Ph.D. in Informatics from Università della Svizzera italiana (USI), Lugano, Switzerland, and a M.Sc. in Computer Engineering from Tehran Polytechnic. During his Ph.D., he visited the CIIR Lab at the University of Massachusetts Amherst, USA. His research interests include conversational search systems, recommender systems, unified search frameworks, and user-centric evaluation methodologies. Notable contributions include work on clarifying questions in open-domain dialogues, contextual suggestion systems, and cross-market recommendation. Aliannejadi has organized major shared tasks and workshops, including the IGLU Contest (NeurIPS 2021) and XMRec Workshop (RecSys 2021). He serves on program committees of top IR conferences like SIGIR, CIKM, and ECIR, and has authored over 50 peer-reviewed publications in these areas. His work has received recognition, including top performance in TREC Contextual Suggestion tracks (2015, 2016). He actively contributes to the IR community through teaching, including courses on Information Retrieval and Human-in-the-Loop Machine Learning at the University of Amsterdam.

• Information Retrieval
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Confidence Duku is a researcher at Wageningen University & Research, specializing in climate resilience and agricultural systems. Their work integrates climate science, hydrology, and machine learning to address food security, deforestation impacts, and flood forecasting in data-scarce regions. Research Interests: Climate change modeling in Eastern Africa Hydrology-guided neural networks for flood forecasting Agricultural resilience (common bean, green gram) under climate stressors Economic impacts of deforestation in Brazil Climate services for financial institutions and SMEs Notable Contributions: Developed frameworks for climate-smart business planning and flood prediction, with a focus on regions like East Africa and Brazil. Their work emphasizes ecosystem services and adaptation strategies. Collaborations: Active in multi-institutional projects, including partnerships with SNV and Copernicus. Led LVVN projects on cascading climate risks and reforestation impacts.

• Climate Change
• Deforestation
• Agricultural Science
• +۵ مورد دیگر

Prof. Khalil Sima'an serves as Professor of Computational Linguistics and Vice Director of Education at the Institute for Logic, Language and Computation (ILLC), University of Amsterdam, within the Faculty of Science. He joined as Assistant Professor in 2003, advanced to Associate Professor in 2011, and attained Full Professorship in 2014. His academic foundation includes a PhD in Computational Linguistics from Utrecht University, establishing his expertise in formal language modeling. Prof. Sima'an's research drives innovation in Computational Linguistics through rigorous exploration of syntactic structures, semantic processing, and machine translation systems. His work bridges theoretical frameworks with practical NLP applications, particularly in parsing technologies and multilingual processing, positioning him as a key contributor to European language technology initiatives. His distinguished recognition includes: Royal Netherlands Academy for Arts and Sciences (KNAW) Fellowship NWO Vidi Grant (2006) for pioneering early-career research NWO Vici Grant (2013) for groundbreaking senior research leadership He actively shapes the field through editorial stewardship of Machine Translation and Journal of Natural Language Engineering , alongside pivotal conference roles including MT Summit XIV Chair (2013) and ACL 2010 area chair for syntax/parsing. As core faculty in the Natural Language Processing & Digital Humanities (NLP&DH) research group, he advances collaborative projects integrating computational methods with humanities scholarship at LAB42's interdisciplinary hub.

• Computational Linguistics
• Natural Language Processing
• Machine Translation
• +۳ مورد دیگر

Pim de Vink is a doctoral researcher at Eindhoven University of Technology , affiliated with the Biomedical Engineering department and Chemical Biology group. Supervised by dr. L.-G. Milroy and prof. L. Brunsveld , his work bridges supramolecular chemistry and chemical biology , focusing on host/guest chemistry for protein complex modulation. Education: B.Sc. in Chemistry (2014) from University of Amsterdam M.Sc. in Biomedical Engineering (2016) from TU/e Internship at Max Planck Institute for Molecular Physiology (2016) on gold-catalyzed synthesis Research Themes: His research develops switchable cucurbituril-based systems for light-controlled enzyme activation and artificial signaling networks. Key areas include protein-protein interaction stabilization , thermodynamic modeling , and allosteric nuclear receptor modulation . Publication Trends: Across JACS , Chemical Science , and RSC Chemical Biology , his work from 2017–2023 emphasizes supramolecular tools for biochemical applications. Notable contributions include 100-fold affinity enhancement of 14-3-3 ligands and UV-responsive cucurbituril release mechanisms . Grants: Funded by Netherlands Organization for Scientific Research (NWO) through Gravity program 024.001.035 and VICI grant 016.150.366.

• Supramolecular Chemistry
• Chemical Biology
• Protein-Protein Interactions
• +۳ مورد دیگر

Thomas Bäck is a Professor at the Leiden Institute of Advanced Computer Science (LIACS) , Leiden University , Netherlands, and a member of the interdisciplinary programme Society, Artificial Intelligence and Life Sciences (SAILS) . His academic career spans roles at Leiden University (1996–present) and leadership positions at the Center for Applied Systems Analysis in Dortmund (1994–2000). Education : Diplom-Informatiker (Computer Science), Technische Universität Dortmund (1990) Dr. rer. nat. (Computer Science), Technische Universität Dortmund (1994) Research Interests : Dr. Bäck specializes in evolutionary computation , machine learning , and their applications in sustainable smart industry and healthcare . Recent work focuses on integrating large language models (LLMs) and quantum computing into optimization frameworks, with projects like CIMPLO (predictive maintenance), ECOLE (experience-based optimization), and SAPPAO (airline operations optimization). Scientific Contributions : His 526+ publications cover evolutionary algorithms, quantum optimization, and LLM-driven design, with recent trends including: Quantum computing (e.g., quantum approximate optimization, quantum advantage challenges) LLM integration (e.g., hyperparameter tuning, mutation control, code evolution graphs) Healthcare and industry (e.g., predictive maintenance, anomaly detection, melt quality prediction) Algorithm benchmarking (e.g., IOHprofiler, MA-BBOB, explainable benchmarking) Scientific Awards : IEEE Fellow (2022) Royal Netherlands Academy of Arts and Sciences (KNAW) member (2021) Academia Europaea member (2022) IEEE Computational Intelligence Society Evolutionary Computation Pioneer Award (2015) Fellow, International Society of Genetic and Evolutionary Computation (2003) Best Ph.D. thesis award, German Society of Computer Science (GI) (1995) Advising and Grants : He supervises Ph.D. candidates in evolutionary computation and machine learning and has secured 7 major grants from organizations like the Dutch Research Council , European Commission , and The Research Council of Norway . His editorial roles include Editor-in-Chief of the Evolutionary Computation Journal and associate editorships in leading AI journals.

• Evolutionary Computation
• Machine Learning
• Natural Computing
• +۵ مورد دیگر

Oscar Tomico Plasencia is an Associate Professor at the Department of Industrial Design at Eindhoven University of Technology (TU/e), specializing in Design Research Methodologies for Posthuman Sustainability. His research challenges anthropocentric design paradigms by positioning designers within complex ecological systems and exploring collaborations with non-human actors like plants. Education: MSc in Industrial Engineering and PhD cum laude from Polytechnic University of Catalonia (Spain). Prior Roles: Assistant Professor at TU/e (2007–2023), Head of Industrial Design Bachelor’s program at ELISAVA (Spain), Co-director of Design for Emerging Futures Master Program, and Co-editor-in-chief of Temes de Disseny Journal . His research focuses on decentering human agency in design through First-person methodologies for ecological engagement Design practices for multi-species cohabitation Regenerative socio-ecological-technical systems Critical fabulation with non-human stakeholders Temporal analysis of design events Recent publications analyze plant studios as dynamic constituencies (2025), backyard practices for urban sustainability (2025), and nature-entangled methodological repositories (2024). His work emphasizes transparency in experimental design processes and ecological accountability. Scientific Awards & Funding: NSERC Discovery Grant #RGPIN-2023-04600 SSHRC Insight Grant #435-2024-0783 Collaborative projects involve institutions across Spain, Japan, Sweden, and New Zealand. He leads the Making With… research group at TU/e and contributes to the EAISI (Eindhoven Artificial Intelligence Systems Institute).

• Posthuman Sustainability
• More-than-human Design
• Design Research Methodologies
• +۲ مورد دیگر

Pascal Mettes is a tenured Assistant Professor at the University of Amsterdam within the Informatics Institute, specializing in Artificial Intelligence. He leads groundbreaking research in hyperbolic deep learning, a field he has significantly advanced through theoretical developments and practical applications in computer vision and multimodal learning. His research focuses on three primary domains: hyperbolic vision-language models that address the hierarchical nature of language-vision relationships; hierarchical deep learning using hyperbolic embeddings that naturally accommodate exponential growth patterns; and robust deep learning in hyperbolic space that improves out-of-distribution detection and network resilience. Mettes has established himself as a leading figure in this emerging field through numerous publications at top-tier conferences including CVPR, ICCV, ICML, NeurIPS, and ICLR. His recent work demonstrates how hyperbolic geometry provides natural solutions to fundamental limitations in modern deep learning, particularly regarding hierarchical data structures that cannot be adequately represented in Euclidean space. The publication trends show increasing impact and recognition in the computer vision and machine learning communities, with multiple papers receiving oral presentations and best paper nominations. Best paper nomination ESWC25 for 'Designing Hierarchies for Optimal Hyperbolic Embedding' Finalist MM 2023 Best Open-Source Software Competition (for HypLL) Multiple reviewer awards across major conferences including CVPR, ICLR, ECCV, ICML, and NeurIPS MM 2016 Best Doctoral Student Award TRECVID 2015 Winner Multimedia Event Detection Benchmark Mettes actively mentors eight PhD students working on hyperbolic learning and related topics, while also securing significant research funding including ELLIs PhD Award, NWO ClickNL, Google Perception Academic Funding, and Data Science Centre PhD Grants. He serves in prominent academic roles as Program Chair for International Conference on Multimedia Retrieval 2026 and has organized multiple workshops on hyperbolic learning at major conferences. His leadership in establishing hyperbolic deep learning as a recognized research direction is evident through his survey paper in IJCV 2024 and the development of the HypLL library for hyperbolic learning.

• Hyperbolic Deep Learning
• Computer Vision
• Vision-Language Models
• +۳ مورد دیگر

Dr. David Goretzko is an Assistant Professor in the Department of Methodology and Statistics at Utrecht University's Faculty of Social and Behavioural Sciences. He leads the Measurement and Machine Learning Lab and specializes in the integration of data science techniques with psychometric theory. His academic journey includes: Ph.D. in Psychological Methods from LMU Munich (2020) M.Sc. in Statistics from LMU Munich (2018) M.Sc. in Psychology from LMU Munich (2016) B.Sc. in Physics from LMU Munich (2015) B.Sc. in Psychology from LMU Munich (2014) Dr. Goretzko's research primarily focuses on the intersection of machine learning and psychometrics. His work addresses critical challenges in factor analysis, measurement invariance, and model fit assessment. He develops innovative methods that combine traditional psychometric approaches with modern data science techniques, particularly in the areas of exploratory factor analysis trees, regularized factor analysis, and cost-sensitive machine learning applications in psychological assessment. His research has significant implications for improving the validity and reliability of psychological measurements across diverse populations. His recent publications reveal a strong trend toward integrating machine learning methodologies with traditional psychometric approaches. A significant portion of his work focuses on factor analysis techniques, particularly addressing the challenge of determining the appropriate number of factors. His research also extensively covers measurement invariance testing across multiple covariates using tree-based approaches, and he has made notable contributions to evaluating model fit in confirmatory factor analysis. The interdisciplinary nature of his work spans psychology, statistics, and computer science. Dr. Goretzko serves as an Associate Editor for the European Journal of Psychological Assessment and is an active reviewer for numerous prestigious journals including Psychological Methods, Behavior Research Methods, and Structural Equation Modeling. He also reviews grant proposals for major funding agencies such as the German Research Foundation (DFG), National Science Foundation (NSF), and Dutch Research Council (NWO). He currently holds a Project Grant from the German Research Foundation (DFG GO 3499/1-1) since 2021. His research program focuses on developing and validating new methodologies for psychological assessment that incorporate machine learning techniques while maintaining psychometric rigor. His work has practical applications in educational measurement, clinical psychology, and organizational assessment. Dr. Goretzko leads the Measurement and Machine Learning Lab at Utrecht University, which focuses on developing innovative methodologies that bridge the gap between traditional psychometrics and modern data science. The lab's research has particular relevance for improving measurement practices in cross-cultural research, educational assessment, and clinical psychology settings where measurement invariance and factor structure validation are critical concerns.

• Latent Variable Modeling
• Machine Learning
• Measurement Equivalence
• +۳ مورد دیگر

Dr. Julia Kamenz is an Assistant Professor (Rosalind Franklin fellow) at the University of Groningen's Faculty of Science and Engineering, where she leads research in the Molecular Systems Biology group within the Groningen Biomolecular Sciences and Biotechnology Institute (GBB). Her work focuses on understanding the molecular mechanisms that regulate cell cycle progression and cell division. Dr. Kamenz received her undergraduate training in Biochemistry at the University of Tuebingen, completed her PhD at the Friedrich Miescher Laboratory of the Max Planck Society under Dr. Silke Hauf (defended February 2015 with highest honors), and conducted postdoctoral research at Stanford University with Prof. James E. Ferrell. Her PhD work was supported by a Boehringer Ingelheim Fonds fellowship, and her postdoc was funded by a German Research Foundation (DFG) Postdoctoral Fellowship. Her research expertise spans cell cycle regulation and dynamics, post-translational modifications, Xenopus laevis model systems, and live cell microscopy. Dr. Kamenz investigates how kinases and phosphatases intricately regulate cell proliferation and division, with particular interest in the molecular mechanisms that ensure faithful chromosome segregation during mitosis. Her recent work has revealed novel insights into mitotic checkpoint signaling, particularly in early embryonic development where these checkpoints appear to function differently than in somatic cells. Dr. Kamenz's publication record demonstrates a strong focus on the dynamics of cell cycle transitions, with recent papers appearing in high-impact journals including Nature, The Journal of Biological Chemistry, and The Journal of Cell Biology. Her research integrates experimental biochemistry, live-cell imaging, and computational modeling approaches to understand complex regulatory networks. ERC Starting Grant (November 2022) NWO Vidi Grant (July 2021) Mansour Postdoctoral Travel Award (2019) Dr. Kamenz has secured significant research funding including an ERC Starting Grant (€1.5 million) and an NWO XS grant (€50,000) for her project "What limits mitotic checkpoint signaling in the early embryo?" Her research contributes to understanding fundamental biological processes with implications for developmental biology and cancer research. She collaborates extensively within the University of Groningen and with international partners, particularly in the areas of cell cycle research and biophysical approaches to biological problems. Dr. Kamenz leads a research group focused on cell cycle regulation within the Molecular Systems Biology division of the Groningen Biomolecular Sciences and Biotechnology Institute. Her lab combines biochemical approaches using Xenopus egg extracts with live-cell imaging and computational modeling to dissect the molecular mechanisms controlling cell division.

• Cell cycle regulation
• Cell division
• Biochemistry
• +۷ مورد دیگر

Mykola Pechenizkiy is a Full Professor at the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e), holding the Data Mining Chair. He also serves as an Adjunct Professor in Data Mining for Industrial Applications at the University of Jyväskylä. His research focuses on predictive analytics, data mining, and responsible AI, addressing real-world challenges in industry, healthcare, and education. He leads the Customer Journey research program at the Data Science Center Eindhoven, emphasizing ethical and transparent analytics. Academically, he holds a PhD from the University of Jyväskylä (2005) and has held visiting researcher positions at institutions like Columbia University and NYU. He has co-authored over 300 peer-reviewed publications and serves on editorial boards and committees for leading conferences (e.g., AAAI, IJCAI). He is the President of the International Educational Data Mining Society (IEDMS). His research interests include concept drift adaptation, sparsity techniques in neural networks, and fairness-aware AI. He has led projects such as the TKI PPS KPN Smart Two initiative and collaborates with industries like ASML, Philips, and Rabobank. His work contributes to UN SDGs, particularly in sustainable development through AI-driven solutions. Awards: Best Demo Paper Award (IEEE ICDE 2023), Best Paper Awards (ALA 2022, LoG 2022), and SensorKDD 2009 recognition. Grants/Projects: Active projects include TKI PPS KPN Smart Two (2019–2025) and Smart One W&I TKI KPN Flagship (2018–2022). Labs/Teams: Affiliated with EAISI Health, SIKS Scientific Board, and the University of Waikato’s AI Institute.

• Data Science
• Machine Learning
• Predictive Analytics
• +۵ مورد دیگر

Barbara J. Grosz serves as Higgins Research Professor of Natural Sciences in Harvard University's School of Engineering and Applied Sciences, renowned for foundational contributions to Artificial Intelligence through pioneering work in dialogue processing and multi-agent collaboration theories applied to human-computer interaction, healthcare coordination, and science education. Her research spans Artificial Intelligence, Natural Language Processing, Multi-agent Systems, and Computational Ethics, with recent focus on improving computer system design for healthcare and education. She co-founded Harvard's Embedded Ethics program, which integrates ethical reasoning into core computer science curricula, reflecting her commitment to responsible AI development. Professor Grosz has earned exceptional recognition including: ACM/AAAI Allen Newell Award (2009) IJCAI Award for Research Excellence (2015) ACL Lifetime Achievement Award (2017) Election to the National Academy of Engineering Membership in the American Philosophical Society Her leadership extends to establishing interdisciplinary research institutions and advancing women in science, demonstrating sustained impact across academia and societal applications of AI.

• Artificial Intelligence
• Natural Language Processing
• Multi-agent Systems
• +۴ مورد دیگر

Francesca Grisoni serves as an Assistant Professor in the Department of Biomedical Engineering at Eindhoven University of Technology (TU/e), where she currently leads the Molecular Machine Learning team. She additionally holds appointments as an ICMS Core member and Associate Professor at EAISI (Eindhoven Artificial Intelligence Systems Institute), reflecting her cross-disciplinary role at the intersection of computational science and biomedical applications. Academic Background : Grisoni completed her Environmental Sciences degree and earned a Ph.D. in 2016 from the University of Milano-Bicocca, where her dissertation focused on interpretable machine learning for molecular property prediction. During doctoral studies, she conducted research at ETH Zurich's Department of Chemistry and Applied Biosciences and the U.S. EPA's National Center for Computational Toxicology. Ph.D., University of Milano-Bicocca, 2016 (Dissertation: Interpretable machine learning for molecular property prediction) Environmental Sciences, University of Milano-Bicocca Her research integrates artificial intelligence, chemistry, and biology to develop computational methods for drug discovery, emphasizing wet-lab experimental validation alongside algorithmic innovation. Key focus areas include overcoming activity cliffs in molecular machine learning, generative modeling for scaffold hopping, and AI-augmented decision-making in therapeutic development, with the ultimate goal of achieving 'better decisions faster' in drug discovery pipelines. Analysis of her recent 2025 publications reveals a concentrated trend toward chemical language models and generative deep learning frameworks, specifically addressing low-data drug discovery challenges through active learning and neural network architectures. These works bridge computer science with pharmacology, targeting bioactivity prediction, molecular representation, and enzyme design while maintaining strong ties to experimental validation. Scientific Awards : Lush Young Researcher Prize Early Career Award 2022 from the Dutch Royal Netherlands Academy of Arts and Sciences (KNAW) ERC Starting Grant (2022) Grants and Supervision : Dr. Grisoni secured the prestigious ERC Starting Grant in 2022 to advance her molecular machine learning research. Institutional records indicate she has supervised 7 students (as shown in TU/e's 'Supervised Work (7)' repository section), though specific names aren't provided in the source material. Her group maintains active industry collaborations, including past engagement with Bracco Pharmaceuticals. Laboratory and Team : The Molecular Machine Learning team operates under the ICMS and EAISI frameworks, merging computational AI development with experimental wet-lab validation. This collaborative unit focuses on fragment-based molecular design, chirality representation (evidenced by fragSMILES work), and high-throughput nanoparticle identification using machine learning, as highlighted in recent press coverage and datasets.

• Artificial Intelligence
• Machine Learning
• Drug Discovery
• +۸ مورد دیگر