جست‌وجوی استاد و پژوهشگر

• Friction Stir Welding
• Materials Science
• Laser Welding
• +۴ مورد دیگر

Bilal Ahmad serves as a Teaching Fellow in the Department of Mechanical and Aerospace Engineering at the University of Strathclyde, United Kingdom. His academic role combines teaching responsibilities with research in advanced materials joining technologies, focusing on computational modeling of welding processes for structural and polymeric materials. Education PhD in Mechanical Engineering from University of Strathclyde (awarded March 2019) with thesis titled Numerical Optimisation of low alloy steel friction stir welding supervised by Professors Galloway and Toumpis Dr Ahmad's research centers on friction stir welding (FSW) and laser-assisted variants, employing numerical modeling to optimize weld quality in structural steel and polyethylene applications. His work addresses critical challenges in material flow dynamics, thermal management, and mechanical property enhancement during joining processes, contributing to advancements in manufacturing efficiency for automotive and aerospace sectors. Analysis of his 2019-2023 publications reveals consistent focus on computational optimization of welding parameters, with significant citation impact (25+ Scopus citations) for his structural steel research. His work bridges theoretical modeling with practical industrial applications, particularly in joining dissimilar materials where conventional techniques face limitations. Dr Ahmad actively contributes to departmental quality initiatives as Academic Staff on the EGCF project (active since June 2024), developing standardized exam guidelines and quality assurance frameworks for Mechanical and Aerospace Engineering programs. He also engages in educational outreach through activities like the MAE Egg Drop Program, demonstrating commitment to engineering education at multiple levels.

Yingqian Zhangمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Deep Reinforcement Learning
• +۷ مورد دیگر

Yingqian Zhang is an Associate Professor in the Information Systems group at the Industrial Engineering and Innovation Sciences department of Eindhoven University of Technology (TU/e). She is affiliated with the Eindhoven Artificial Intelligence Systems Institute (EAISI), specifically with the EAISI High Tech Systems and EAISI Foundational groups. Her research focuses on applying Artificial Intelligence to solve complex decision-making problems across various domains including logistics, transportation, manufacturing, and e-commerce. Dr. Zhang received her PhD in Computer Science from the University of Manchester, UK. Prior to joining TU/e, she served as an Assistant Professor in the Econometrics Institute at Erasmus University Rotterdam and as a postdoc researcher in the Algorithmics group at TU Delft. She was also a visiting professor at the Institute for Advanced Computer Studies at University of Maryland, College Park, USA. Her research expertise lies at the intersection of Artificial Intelligence and optimization, with particular focus on machine learning, deep reinforcement learning, and trustworthy data-driven optimization. Dr. Zhang develops socially aware algorithms that can optimize decisions in data-rich environments. Her work bridges the gap between theoretical AI advancements and practical applications in industrial settings, addressing real-world challenges through innovative algorithmic solutions. She is particularly interested in how AI can support human decision-making while maintaining transparency and trustworthiness. Dr. Zhang's recent publications reveal a strong trend toward applying graph neural networks and reinforcement learning to complex scheduling and optimization problems. Her work demonstrates increasing sophistication in handling stochastic elements in decision-making processes, with applications spanning healthcare diagnostics, logistics, transportation, and manufacturing. She has made significant contributions to the field of neural combinatorial optimization, particularly for job shop scheduling problems and vehicle routing. Dr. Zhang has received several prestigious awards recognizing her contributions to the field: Winner of the MLVRP2023 GECCO competition (2023) Best Paper Award from Omega-International Journal of Management Science (2017) Best Industrial Paper Award (2020) Best Student Paper Award (2019) Best Student Paper Award of ICAART 2022 (2022) As a dedicated mentor, Dr. Zhang supervises numerous PhD students including Mohsen Abbaspour Onari, Abdo Abouelrous, Luca Begnardi, Xia Jiang, Chengpeng Hu, Minshuo Li, Robbert Reijnen, Jesse van Remmerden, Bart von Meijenfeldt, Ya Song, and Igor Smit. Her research is supported by various grants, including the LEO (Learning and Explaining Optimization) project co-funded by Holland High Tech | TKI HSTM via the PPP allowance scheme for public-private partnerships. Dr. Zhang actively contributes to the academic community as the Chair of the Benelux Association for Artificial Intelligence (BNVKI) and as a member of the Technical Board for the European Big Data Value Association (BDVA). She serves as an associate editor for the "Annals of Mathematics and Artificial Intelligence" journal and participates in the technical Program Committee for major AI conferences such as IJCAI, AAAI, AAMAS, and ECAI. She is also on the executive committee of the Data Science meets Optimisation (DSO) working group of EURO to promote collaboration between AI and Operations Research communities.

Nicolai Siim Larsenمشاهده پروفایل

استادیار

• Statistics
• Data Science
• Stochastic Processes
• +۳ مورد دیگر

Nicolai Siim Larsen is an Assistant Professor (Tenure track) in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), specializing in statistical modeling with applications in finance and healthcare analytics. His work bridges theoretical statistics and real-world data challenges. Education : Ph.D. in Stochastic Financial Models based on Matrix-Analytic Methods, Technical University of Denmark (2022) Dr. Larsen's research centers on multivariate phase-type distributions and matrix-analytic methods , with significant contributions to financial risk modeling and healthcare analytics . His expertise spans survival analysis, sensor data processing, and price optimization frameworks, addressing complex stochastic phenomena in insurance and disease progression monitoring. Analysis of his publications reveals a cohesive focus on advancing numerical computational methods for multivariate data structures. His 2022 Ph.D. thesis established foundations for joint density functions and infinitely divisible distributions, with subsequent work directly applied to commercial pricing strategies and edema patient monitoring systems. Statistical analysis and price optimisation of DJ rental services (2022) AI Denmark: Monitoring disease progression in Oedema patients (2022) KomDigital: Optimal pricing strategies for price monitoring (2022) Stochastic Financial Models based on Matrix-Analytic Methods (PhD project 2018-2023) He actively contributes to academic service as an internal examiner for Statistical Genetics (2025) and instructor for R-based data science workshops, demonstrating commitment to both research leadership and pedagogical development within DTU's Statistics and Data Analysis section.

• High Performance Computing
• HPC Interconnection Networks
• Enterprise Data Centre Networks
• +۶ مورد دیگر

Ernst Gunnar Gran is Associate Professor at the Department of Information Security and Communication Technology at the Norwegian University of Science and Technology (NTNU), where he heads the communication technology discipline. He also holds an adjunct research scientist position at Simula Research Laboratory, where he headed the Cloud department until December 2016. His research spans high performance computing (HPC), HPC interconnection networks, enterprise data centre networks, cloud computing, and data-intensive processing in multi-clouds. He serves as the Scientific Leader of Communication Technologies in the RCN-funded infrastructure project eX3 (Experimental Infrastructure for Exploration of Exascale Computing) and has significant experience with both RCN-funded and EU-funded research projects, including the H2020 project Melodic (Multi-cloud Execution-ware for Large-scale Optimised Data-Intensive Computing). Gran received his M.Sc. and Ph.D. degrees in computer science from the Department of Informatics, University of Oslo, in 2007 and 2014, respectively. Both theses focused on different aspects of resource management in high performance interconnection networks. He previously headed the RCN-funded project ERAC (Efficient and Robust Architecture for Big Data Clouds) and led the design, implementation, and deployment of the multi-homed IP-based research testbed NorNet Core. Gran also has several years of experience as a system administrator and scientific programmer. His research interests center on the intersection of high performance computing and networking, with particular focus on anomaly detection in time series data, HPC interconnection networks, network virtualization, and cloud computing infrastructure. His work demonstrates a consistent evolution from fundamental networking research to applied solutions for modern computing challenges, particularly in IoT security and smart home applications. His recent publications show a strong emphasis on developing lightweight, real-time anomaly detection systems using deep learning techniques. Analysis of his publication trends reveals a clear progression from traditional HPC networking research toward time series anomaly detection applications, particularly for IoT systems. His 15 most recent publications show dual focus areas: approximately 60% concentrate on anomaly detection methods for time series data (particularly for IoT applications), while the remaining 40% maintain his foundational work in HPC networking, virtualization, and cloud infrastructure. This evolution demonstrates his ability to adapt core networking expertise to emerging application domains while maintaining technical depth. While no specific scientific awards are mentioned in the provided text, Gran's leadership roles in significant research projects (eX3, Melodic, ERAC) indicate recognition of his research capabilities within the academic and research funding communities. His position as Scientific Leader of Communication Technologies in the RCN-funded eX3 project further demonstrates his standing in the Norwegian research community. Gran's teaching responsibilities include serving as course coordinator for DCSG1006 Data Communication and Networks, DCSG2001 Interconnected Networks and Network Security, and Networks: Administration, Programming and Security. His research leadership extends to significant grant-funded projects, including the RCN-funded eX3 infrastructure project and the EU H2020 Melodic project. His previous leadership of the ERAC project and the NorNet Core research testbed demonstrates sustained ability to secure and manage substantial research funding. His laboratory and team affiliations include the Department of Information Security and Communication Technology at NTNU, where he heads the communication technology discipline, and Simula Research Laboratory, where he maintains an adjunct position. The NorNet Core research testbed, which he led the development of, represents a significant infrastructure contribution to the networking research community. His current work with the eX3 project suggests ongoing involvement in experimental infrastructure for exascale computing exploration.

Harun Siljakمشاهده پروفایل

استادیار

• Complex Systems Science
• Cybernetics
• Science and Technology Studies
• +۱ مورد دیگر

Harun Siljak is a CONNECT Funded Investigator and Assistant Professor in Embedded Systems, Control and Optimisation at the School of Engineering, Trinity College Dublin . He holds a PhD in Electrical and Electronics Engineering from International Burch University (2015) and bachelor/master degrees in Automatic Control and Electronics from the University of Sarajevo (2010/2012). Research focuses on merging complex systems science, cybernetics, and reversible computation for applications in wireless communications, quantum computation, molecular communications, and neuronal communications Current work explores the interface of technology and society, examining effects of emerging technologies Senior member of IEEE and Associate Editor for IEEE Transactions on Molecular, Biological, and Multi-Scale Communications

Joshua Tanمشاهده پروفایل

استادیار

• Financial Mathematics
• Mathematics
• Actuarial Science
• +۳ مورد دیگر

Dr Joshua Tan serves as an Assistant Professor in the School of Mathematical and Computer Sciences at Heriot-Watt University's Malaysia Campus, where he conducts research and teaching in mathematical disciplines. His research spans core mathematical fields with emphasis on: Financial Mathematics and Actuarial Science applications Number Theory, particularly its historical development Matrix Theory through rank preservers problems Administratively, Dr Tan functions as timetable optimiser, students' club liaison, and impact statement coach, supporting institutional operations while maintaining active research engagement as evidenced by his ORCID profile and participation in academic workshops like the 2022 PURE Workshop for HWUM MACS.

Slobodan Kalajdzhiskiمشاهده پروفایل

استاد

• Bioinformatics
• Systems Biology
• Knowledge Discovery in Data
• +۴ مورد دیگر

Slobodan Kalajdzhiski Ph.D. , born in 1976 in Ohrid, is a full-time Professor at the Faculty of Computer Science and Engineering (FINKI) , Ss. Cyril and Methodius University in Skopje, North Macedonia. He coordinates three master-level study programmes: Content-Based Retrieval , Intelligent Information Systems , and Bioinformatics , and is actively involved in teaching and curriculum development. Education: B.Sc. Electrical Engineering – Skopje, 2000 M.Sc. Electrical Engineering – Skopje, 2004 Ph.D. Electrical Engineering & Information Technologies – Skopje, 2008 Research Interests: His research portfolio centres on bioinformatics and systems biology , leveraging data mining and knowledge discovery techniques to analyse complex biological data. He has designed and optimised data-warehouse architectures and developed methodologies for object-oriented analysis and design of large-scale information systems. These interests are tightly coupled with the advanced database courses he delivers at both undergraduate and postgraduate levels. Research Activity & Grants: Over his career he has participated in eight funded research projects and authored more than thirty peer-reviewed papers in international and domestic journals and conferences. While specific article titles are not listed in the supplied text, the thematic focus of these publications aligns with his declared fields of interest, evidencing a sustained contribution to computational biology and data-driven system design. Teaching & Advising: Beyond his professorial duties, he has previously served as demonstrator, junior assistant, and assistant professor, progressively shaping the curriculum in programming languages, object-oriented systems, databases, and information-system design. He currently mentors master students within the specialised programmes he coordinates.

• Aerospace Engineering
• Flight Mechanics and Control
• Airworthiness and Certification
• +۶ مورد دیگر

Manuela Battipede is Associate Professor of Flight Mechanics & Control at the Politecnico di Torino , Department of Mechanical and Aerospace Engineering (DIMEAS). Since 2002 she has led research and teaching in aerospace guidance, airworthiness, neural-network-based virtual sensors, and trajectory optimisation, coordinating EU H2020 and Clean Sky projects, industrial airworthiness certification contracts, and supervising PhD students in aerospace engineering. Education & Academic Career Joined Politecnico di Torino as a confirmed Associate Professor (Prof.ssa Associata Confermata). Visiting Researcher, West Virginia University, USA (April–September 2002). Research Interests Her work integrates control theory , flight mechanics , and artificial-intelligence-based sensing to enhance safety and efficiency of air and space vehicles. Key themes include: 4-D trajectory optimisation for climate-neutral aviation. Certifiable virtual air-data systems using neural networks. Flutter suppression and intelligent flight control for fixed-wing and rotary-wing aircraft. Low-thrust orbital mechanics, collision avoidance, and end-of-life disposal for satellites. Lighter-than-air platforms and VTOL hybrid drones for earth-observation and fire-monitoring missions. Scientific Awards & Recognition PoCN – Proof of Concept Network (2015), AREA Science Park, Italy. Regular evaluator for SESAR Joint Undertaking, EU H2020, and European Commission programmes. Doctoral Advising & Funding Since 2011 she has served on the PhD board of the Aerospace Engineering doctorate at Politecnico di Torino, currently supervising: Giorgio Antonio Orlando (39th cycle, 2023–) Gabriele Tarascio (39th cycle, 2023–) She has been Scientific Director of >20 competitively funded projects (EU Clean Sky MIDAS, ESA, MIUR-PRIN, EASA certification contracts, etc.) and commercial consultancy contracts exceeding €3 M. Laboratories & Teams Battipede leads the Modelling, Simulation and Control of Aircraft research group at DIMEAS, managing real-time hardware-in-the-loop test rigs, CubeSat development platforms, and an integrated multi-aircraft simulation laboratory for education and industrial validation.

• Computer-Aided Design
• Additive Manufacturing
• Machine Learning
• +۵ مورد دیگر

Dr. Murat AYDIN is a full-time Assistant Professor (Dr. Lecturer) at Karabük University, Faculty of Technology, Department of Industrial Design Engineering, where he has also served as Vice-Dean since 2022 and Head of Department since 2021. His entire academic career since 2008 has been spent at Karabük University and briefly at Düzce University. Education PhD in Mechanical Education, Karabük University, 2019 MSc in Mechanical Education, Karabük University, 2009 BSc in Design & Construction Teaching, Bülent Ecevit University, 2006 Associate Degree in Technical Programs, Selçuk University, 2003 Research Interests His work centres on the intersection of digital design and manufacturing, spanning computer-aided design & manufacturing , additive manufacturing , machine learning , image processing , and finite-element analysis . Within these themes he investigates process optimisation, mechanical characterisation of printed parts, formability of sheet metals, and advanced sensing techniques such as digital image correlation. Across 41 publications and 33 conference presentations from 2008 to 2024, a clear evolution is visible: early studies on hydraulic sheet-metal forming have gradually expanded to embrace 3D printing, composite filaments, and machine-learning-driven design optimisation, positioning him at the forefront of Industry 4.0-oriented research. Scientific Recognition & Service Associate Editor, International Journal of 3D Printing Technologies and Digital Industry (2022–present) Editorial board member and frequent organiser of national & international congresses on 3D printing and industrial design Peer reviewer for Journal of Cleaner Production and other leading journals Advising & Funding He currently supervises four master’s students on topics ranging from medical-device prototyping to the use of 3D printing in composite aerated-concrete production. His research has been funded by five competitive grants, including two TÜBİTAK-supported national projects (total ≈ ₺1.8 M) and an EU-UNESCO project on 3D printer design curricula. Laboratory & Team He leads the Additive Manufacturing & Digital Design Laboratory at Karabük University, a facility equipped with multi-material FDM printers, high-resolution DIC systems, and tensile-testing rigs, hosting a multidisciplinary team of graduate researchers and industry collaborators.

• Communication Networks
• Decentralized Algorithms
• Large Deviations
• +۵ مورد دیگر

Ayalvadi Ganesh is a Lecturer in the Department of Mathematics at the University of Bristol's School of Mathematics, where he teaches advanced courses including Complex Networks, Stochastic Optimisation, and Queueing Networks. His academic work bridges theoretical mathematics with practical network applications. His research spans communication networks , decentralized algorithms , and stochastic modeling , with core expertise in large deviations theory , random graphs , queueing systems , and information theory . Recent work demonstrates strong focus on multi-agent bandit problems, epidemic modeling on networks, and latency optimization in distributed systems, reflecting interdisciplinary applications from cybersecurity to biological networks. Analysis of his 15 most recent publications reveals dominant trends in decentralized decision-making (38% of works), network epidemics/rumor spreading (27%), and stochastic optimization (20%), with increasing crossover into machine learning and biological applications since 2020. Best Paper award at ACM SIGMETRICS 2010 for 'Load balancing via random local search in closed and open systems' His teaching portfolio includes graduate-level courses on Complex Networks, Stochastic Optimisation, and Queueing Theory, with documented emphasis on connecting theoretical foundations to real-world network challenges. While specific grant details aren't provided in source materials, his publication pattern suggests sustained research funding in network science and stochastic systems.

Daniel Herringمشاهده پروفایل

پژوهشگر ارشد

• Multi-objective Optimisation
• Evolutionary Computation
• Machine Learning
• +۵ مورد دیگر

Dr Daniel Herring is a Research Fellow in Industrial Mathematics at the School of Mathematics, University of Birmingham, working within Professor Fabian Spill's research group. His position focuses on applied mathematical research bridging theoretical computation and real-world industrial applications. His academic qualifications include: MSci in Natural Sciences from the University of Exeter (2017), with thesis work on optimisation methods for Alzheimer's EEG data modeling PhD in Computer Science from the University of Birmingham (2023), completed under the Priestley Scholarship Award with extended research at the University of Melbourne Dr Herring specializes in dynamic multi-objective optimisation for both continuous and combinatorial problems, advancing evolutionary computation frameworks. His work integrates evolutionary machine learning with novel classification algorithms, targeting applications in healthcare diagnostics, energy infrastructure optimization, and social network dynamics. Current projects address building occupancy optimization and dissociation analysis in adolescent populations, while upcoming research explores clean-air turbulence prediction and information diffusion in graph-based social networks. Scientific recognition includes: Priestley Scholarship Award for doctoral research excellence He actively contributes to the international computer science community through presentations at premier conferences including GECCO and CEC. Dr Herring's research group collaborates on cross-disciplinary industrial mathematics challenges with strong emphasis on translational impact in public health and infrastructure systems.

Alessandro Di Giorgioمشاهده پروفایل

دانشیار

• Smart grids and micro-grids control
• Model Predictive Control for energy systems
• Integration of renewable generation and storage
• +۳ مورد دیگر

Alessandro Di Giorgio is Associate Professor of Automatic Control at the Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG) , Sapienza University of Rome, where he teaches courses on Automation, Network Control and Handling, and Modeling & Simulation. He coordinates the Smart Energy Research Group of the Consorzio per la Ricerca nell’Automatica e nelle Telecomunicazioni and acts as scientific referent of the university start-up Applied Research to Technologies . Education Ph.D. in Systems Engineering, Sapienza University of Rome, 2010 M.Sc. in Physics (110/110 cum laude), Sapienza University of Rome, 2005 Research interests revolve around advanced control architectures for future power systems, with particular emphasis on smart-grid planning and real-time operation , model-predictive control of micro-grids , large-scale integration of renewable generation and storage , stochastic optimisation of electric-vehicle charging infrastructures , and cyber-physical security against malicious attacks . His work combines systems theory with practical implementations validated within several European and national projects. Recent publications (2023-24) demonstrate a clear trend toward data-driven and stochastic model-predictive strategies that coordinate millions of grid-edge resources—electric vehicles, batteries, renewable plants—while guaranteeing economic efficiency, reliability and resilience of distribution networks. Projects & Technology Transfer Scientific responsible in 10 EU-funded research projects on smart grids and electromobility Principal Investigator of Italian academic and Industria 2015 programmes Co-founder & scientific reference, start-up Applied Research to Technologies (technology transfer on EV-charging optimisation) He is member of the Networked Systems Cybersecurity initiative and serves as reviewer and organiser for major IEEE conferences on power systems and control.

• Discrete Optimisation
• Scheduling Theory
• Operations Research
• +۷ مورد دیگر

Frank Werner is apl. Professor (Adjunct Professor) of Mathematical Optimisation at the Otto von Guericke University Magdeburg, Faculty of Mathematics, Institute for Mathematical Optimization. Since 1989 he has been a permanent member of this institute, leading DFG- and EU-funded projects, supervising international doctoral students and post-doctoral researchers, and serving on the editorial boards of more than a dozen journals. Education 1961–1973: Nordpark-Oberschule, Clara-Zetkin-Oberschule, EOS Geschwister Scholl, Magdeburg (Abitur ‘Ausgezeichnet’) 1975–1980: Diploma in Mathematics, Technische Hochschule Magdeburg (Dipl.-Math., grade ‘Ausgezeichnet’) 1980–1983: Doctoral studies, TH Magdeburg (Dr. rer. nat., Summa cum laude) 1987–1989: Habilitation, TH Magdeburg (Dr. rer. nat. habil.) Research Interests Werner’s work lies at the intersection of applied mathematics, operations research and computer science. He designs exact and heuristic algorithms for NP-hard scheduling and sequencing problems, analyses the stability of solutions under interval data uncertainty, and applies these techniques to real-world domains such as machine scheduling, vehicle routing, train timetabling, supply-chain coordination, and healthcare logistics. Recent activities extend to the use of machine-learning techniques to enhance optimisation algorithms. Scientific Awards & Honours Best Paper Award, International Journal of Production Research Best Paper Award, IISE Transactions NOC Prize 2009, INCOM Symposium Moscow, for work on hierarchical mobile-robot scheduling Supervision, Grants & Service Since 1992 Werner has coordinated numerous DFG and EU projects and acted as principal investigator for international collaborations involving Belarus, Russia, Poland, the USA, Mexico, Iran, Nepal, China and Finland. He has mentored Alexander von Humboldt fellows and long-term visiting scholars, and currently serves as Editor-in-Chief of the MDPI journal Algorithms , Associate Editor for Journal of Scheduling and International Journal of Production Research , and on the editorial or advisory boards of more than ten further journals. Laboratory & Teams He heads the Scheduling & Discrete Optimisation group within the Institute for Mathematical Optimization, maintaining active partnerships with Tribhuvan University (Nepal), Autonomous University of Baja California (Mexico), Belarusian State University, École des Mines de Saint-Étienne (France), University of Alabama in Huntsville (USA), and many others.

Benedikt Wirthمشاهده پروفایل

استاد

• Optimisation and calculus of variations
• image processing
• scientific computing
• +۹ مورد دیگر

Prof. Dr. Benedikt Wirth is a Professor of Mathematics at the University of Münster, Germany, affiliated with the Institute for Analysis and Numerics within the Department of Mathematics and Computer Science. He is an active researcher and educator specializing in optimization and calculus of variations, with significant contributions to mathematical imaging and shape analysis. His research interests include image processing, scientific computing, numerical analysis, optimization, shape spaces, geodesics in shape space, variational methods, elastic deformation, and optimal transport. Wirth has developed innovative mathematical frameworks for shape analysis, particularly focusing on Riemannian metrics for shape spaces and variational approaches to shape comparison and optimization. His recent publications (2023-2025) demonstrate continued leadership in mathematical optimization, with particular focus on PET reconstruction, dimension reduction techniques, manifold embeddings, and branched transport theory. His work bridges theoretical mathematics with practical applications in medical imaging and computer vision, showing particular strength in connecting geometric analysis with computational methods. CRC 1450 - A05: Targeting immune cell dynamics by longitudinal whole-body imaging and mathematical modelling CRC 1450 - A06: Improving intravital microscopy of inflammatory cell response by active motion compensation EXC 2044 - C1: Evolution and asymptotics EXC 2044 - C2: Multi-scale phenomena and macroscopic structures EXC 2044 - C3: Interacting particle systems and phase transitions EXC 2044 - C4: Geometry-based modelling, approximation, and reduction Prof. Wirth actively supervises numerous bachelor's and master's students, with over 40 theses completed under his guidance since 2015. His teaching portfolio includes courses on inverse problems, numerical methods for partial differential equations, shape spaces, optimization, and optimal transport. He has consistently maintained an active research program while contributing significantly to the education of the next generation of mathematicians.

• Theoretical Chemistry
• Quantum Chemistry
• Computational Chemistry
• +۶ مورد دیگر

Yoshifumi Nishimura is an Assistant Professor at Waseda University’s Research Institute for Science and Engineering and concurrently serves in Tokyo University of Science’s Faculty of Science, Division of Applied Chemistry. He completed both his bachelor’s (2008) and doctoral degrees (2013) in chemistry at Nagoya University, followed by post-doctoral research at National Chiao Tung University, Taiwan, and a project-researcher position at the Institute for Molecular Science. Education: Doctor of Science, Nagoya University, 2013 Bachelor of Science, Nagoya University, 2008 Research Interests: Nishimura develops linear-scaling quantum chemical methodologies, especially the divide-and-conquer density-functional tight-binding (DC-DFTB) approach, enabling million-atom simulations on supercomputers. His work spans proton-transfer dynamics in water, ice and proteins, CO₂ absorption chemistry, battery electrolytes, photochemical events in retinal proteins, and growth mechanisms of carbon nanotubes and graphene nanoribbons. Awards: CSJ Presentation Award, Chemical Society of Japan, 2017 President’s Award, Nagoya University, 2008 Software & Leadership: He is the lead developer of the open-source DCDFTBMD package, which has been optimised for the K-computer and Fugaku, and he serves on the editorial committee of the 分子シミュレーション学会 journal “アンサンブル” and on organising committees of domestic theoretical-chemistry conferences.