Find Academic Supervisors

• Energy Systems Optimization
• Conceptual Process Design
• Life Cycle Assessment
• +8 more

Prof. Dr. André Bardow is a Full Professor in Energy and Process Systems Engineering at ETH Zurich , leading research at the intersection of thermodynamics, machine learning, and sustainable energy systems. Previously, he held professorships at RWTH Aachen University (2010-2020) and TU Delft (2007-2010). He also served as part-time director at Forschungszentrum Jülich (2017-2022) and visiting professor at UC Santa Barbara (2015/16). His work focuses on energy systems optimization , computer-aided molecular design , and CO2 capture & utilization . PhD from RWTH Aachen University Current ETH Zurich affiliation Former roles at RWTH Aachen, TU Delft, Jülich Research Center His research integrates machine learning with thermodynamic modeling to optimize processes like crystallization and electrochemical cooling . Recent publications demonstrate advancements in solvent design, CO2 transport LCA, and ORC working fluid optimization. He chairs the VDI Technical Committee for Thermodynamics (2016-2024) and has received multiple awards including the Covestro Science Award and Arnold-Eucken-Award . Current projects address carbon circular economies , electrified chemical production , and AI-driven process optimization . His lab at ETH Zurich develops cutting-edge technologies like ML-CAMPD frameworks for sustainable separation processes and photoacid-based CO2 capture systems. Funding from the H2020 Systemic Expansion of Circular Ecosystems (grant 101036854) supports these initiatives. 2024 Clarivate Highly Cited Researcher 2022 Inaugural Lecture: "To sustainability and beyond: A computer-animated story on energy & chemicals" Recipient of multiple teaching and research excellence awards

Erik RodnerView profile

Professor

• Machine Learning
• Computer Vision
• Data Science
• +5 more

Prof. Dr. Erik Rodner is a faculty member at the University of Applied Sciences Berlin (HTW Berlin), where he serves as a Professor for Machine Learning and Data Science. He also contributes to the School of Engineering Sciences - Technology and Life. His research spans computer vision, machine learning, and biomedical applications, with a focus on learning with limited data, robust visual recognition models, and medical image analysis. He has developed innovative methods for medical diagnostics, industrial classification, and anomaly detection. Recent publications (2025-2016) highlight his expertise in visual in-context learning, semi-weakly segmentation, and active learning frameworks. He has collaborated with institutions such as ZEISS Group, Friedrich Schiller University Jena, and UC Berkeley. Scientific Awards: Award for Excellent Teaching (2023)

Jörn MeissnerView profile

Professor

• Supply Chain Management
• Pricing Strategy
• Revenue Management
• +5 more

Prof. Jörn Meissner, PhD, is a Full Professor of Supply Chain Management & Pricing Strategy at Kühne Logistics University (KLU) since 2011. He holds a PhD and Master’s in Management Science from Columbia Business School and a Diploma in Business from University of Hamburg . As an academic and entrepreneur, he founded Manhattan Review and Lancaster Executive . Education: PhD in Management Science, Columbia University (2005) Master of Philosophy, Columbia University (2005) Diplom-Kaufmann, University of Hamburg (1997) Research Expertise: Focus on stochastic and dynamic decision-making using mathematical optimization and machine learning Key projects: Global supply chain optimization , Inventory control , Revenue management , and Operations & service management Industry collaborations with British Telecom , British Airways , Apple Europe , and SAP Germany Publication Trends: Recent work addresses intermittent demand forecasting for spare parts, lateral transshipment optimization , and risk-sensitive capacity control Historical contributions include progressive interval heuristics for multi-item lot sizing and dynamic pricing with customer choice models Teaching Experience: Previously held academic positions at Lancaster University Management School , University of Hamburg , and University of Mannheim Developed MBA electives in Advanced Decision Models , Supply Chain Management, and Revenue Management

Jochen LinßenView profile

Professor

• Energy Systems
• Renewable Energy
• Hydrogen Technology
• +3 more

Prof. Dr.-Ing. Jochen Linßen is a Professor at the Institute of Climate and Energy Systems (ICE) , specifically within the Jülich Systems Analysis (ICE-2) group at the Research Center Jülich GmbH. His work focuses on advancing energy systems modeling, renewable energy integration, and hydrogen technology. Key research areas include the techno-economic analysis of energy transition pathways, hydrogen cost-potentials, and the interplay between energy systems and transportation networks. His expertise spans climate policy, infrastructure planning, and the development of frameworks for long-term mobility demand modeling. He has contributed to high-resolution assessments of renewable energy resources and the evaluation of green hydrogen’s role in decarbonizing industries. Recent work emphasizes AI-driven risk mitigation in energy systems and the impact of natural hazards on energy infrastructure resilience. Research Interests: Modeling of energy systems and hydrogen networks Renewables integration and curtailment challenges Transportation electrification and hydrogen fueling infrastructure Climate policy and decarbonization strategies Key Projects: ETHOS. MODE. regional framework for mobility demand analysis Global assessments of wind and solar power potentials Hydrogen supply chain optimization for Germany and Europe His collaborative approach bridges engineering, economics, and environmental science to address systemic challenges in energy transitions. Ongoing efforts include participatory mapping of green hydrogen potentials in sub-Saharan Africa and analysis of automated driving’s impact on road networks.

• Geographic Information Systems (GIS)
• Map Generalization
• Spatial Data Mining
• +3 more

Jan-Henrik Haunert is a Professor at the University of Bonn, affiliated with the Institute of Cartography and Geoinformatics within the Faculty of Civil Engineering and Geodetic Science. His research focuses on map generalization, geographic information systems (GIS), and optimization techniques in cartography. He has contributed to projects funded by the German Research Foundation (DFG), particularly in deriving scale-dependent representations of geographic data. His work emphasizes logical consistency, semantic accuracy, and quality assessment in geospatial data processing. Key research areas include land cover map generalization, spatial data integration (e.g., volunteered geographic information), and rule-based incremental generalization. He has developed methodologies leveraging mixed-integer programming and straight skeleton algorithms for cartographic automation. His publications span peer-reviewed journals like GeoInformatica and Photogrammetrie - Fernerkundung - Geoinformation , as well as conference proceedings such as AGILE and ACM-GIS. Haunert’s contributions address challenges in geospatial data quality, including polygon simplification and river dataset matching. He has also explored applications in vehicle localization and virtual reality systems like the GeoScope for urban planning. His work bridges theoretical GIScience with practical applications in cartography and geoinformatics.

Martin SchmidtView profile

Professor

• Mathematical Optimization
• Energy Systems
• Mixed-Integer Nonlinear Optimization
• +3 more

Martin Schmidt is a Full Professor (W3) for Nonlinear Optimization at the Department of Mathematics, Trier University, since 2019. He has held leadership roles in research training groups and international committees, focusing on mathematical modeling and optimization of energy systems, gas networks, and market equilibria. His work bridges mixed-integer nonlinear optimization , bilevel optimization , and robust methods with applications to real-world energy challenges. Education: PhD in Mathematics (2013), Diplom in Mathematics (2008), both from Leibniz University Hannover. Editorial Roles: Editorial Board member of Journal of Optimization Theory and Applications and Optimization Letters , Associate Editor for OR Spectrum and EURO Journal on Computational Optimization . His research integrates complex physical systems (e.g., gas transmission networks) with game-theoretic models to analyze energy markets. Recent publications emphasize robust optimization , decomposition techniques , and machine learning integration in bilevel frameworks. Awards highlight his contributions to gas market feasibility , linear bilevel optimization , and practical applications in energy systems. Collaborations span institutions like Universidad Zaragoza, Sapienza University, and Forschungszentrum Jülich.

Andrea WaltherView profile

Professor

• Nonlinear Optimization
• Algorithmic Differentiation
• Mathematical Optimization
• +3 more

Andrea Walther is a Professor of Mathematical Optimization at the Humboldt University of Berlin, holding a position within the Faculty of Mathematics and Natural Sciences. She leads the Mathematical Optimization research group at the Institute of Mathematics, focusing on algorithmic differentiation, nonlinear optimization, and applied mathematics. Her academic journey includes a Diploma in Business Mathematics (1996, University of Bayreuth), a PhD (1999, TU Dresden), and habilitation (2008, TU Dresden). She has held roles such as Junior Professor at TU Dresden (2007–2008) and Professor at the University of Paderborn (2009–2019) before joining Humboldt in 2019 as a MATH+ Professor. Education : 1991–1996: Studies in Business Mathematics, University of Bayreuth 1996: Diploma in Business Mathematics, University of Bayreuth 1999: PhD in Mathematics, TU Dresden 2008: Habilitation, TU Dresden Her research interests center on optimization methods, particularly algorithmic differentiation (e.g., ADOL-C software), nonsmooth optimization, and applications in engineering and machine learning. She leads initiatives like the Cluster of Excellence MATH+ and contributes to projects such as the Transregio 154. Key Projects: Co-PI of DFG Project 'Mixed-integer non-smooth optimization for gas market problems' (2020–2022) Principal Investigator in MATH+ Projects (EF3-7, AA2-7) Co-developer of ADOL-C, a widely used tool for algorithmic differentiation Notable awards include being a SIAM Fellow. Her work bridges theoretical advancements and practical applications, with contributions to energy sector optimization, inverse problems, and computational frameworks for solving complex systems.

Matthias BeckerView profile

Professor

• Human-Computer Interaction
• Simulation and Modeling
• Distributed Systems
• +6 more

Matthias Becker is a Professor at the Institute for Practical Computer Science within the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover, where he has been a core member of the Human-Computer Interaction group since 2019. He serves as Internship Coordinator for Computer Science and Computer Engineering and holds key roles in the Computer Science Examination Board and Selection Committee, actively shaping academic governance and student development. His academic journey began with PhD studies at the University of Bremen (1996-2000) supported by a DFG grant, followed by a postdoctoral permanent position at Leibniz University Hannover (2000-2019), an Associated Assistant Professor role at École des Mines de Nantes (2000), and a Habilitation in Computer Science in 2013. This foundation enabled his transition to a full professorship in 2019. Becker's research spans Human-Computer Interaction, Simulation and Modeling, and Bio-inspired Computing, with applications in agriculture, renewable energy, and manufacturing. His work integrates distributed systems, optimization algorithms, and wireless sensor networks to solve complex real-world problems, such as greenhouse monitoring, wind farm logistics, and tire noise reduction. Recent publications reveal a strategic focus on practical validation of simulation models and cross-domain applications of nature-inspired algorithms. His 15 most recent publications (2018-2024) demonstrate consistent innovation in applying simulation techniques to offshore wind farm installation, agricultural pest management, and sports science. These works emphasize real-world validation, collaborative problem-solving, and the development of domain-specific optimization frameworks that bridge theoretical algorithms and industrial implementation. As Internship Coordinator, Becker facilitates critical industry-academia connections for students, while his examination board responsibilities ensure rigorous academic standards. His leadership in the Human-Computer Interaction group drives research on interactive systems for agriculture, energy, and health, with particular emphasis on user-centered design in complex operational environments like wind farm logistics and greenhouse automation.

• Mixed-Integer Optimization
• Combinatorial Optimization
• Discrete Mathematics
• +3 more

Timm Oertel is a Professor in the Department of Data Science at Friedrich Alexander University Erlangen-Nuremberg (FAU), holding the Chair of Analytics & Mixed-Integer Optimization. His office is located in Room 03.344 at Cauerstraße 11, Erlangen, and he can be contacted via email at timm.oertel@fau.de or phone at +49 9131 85-67313. His research focuses on mixed-integer optimization, combinatorial optimization, and discrete mathematics, with significant contributions to sparse solutions in lattices and semigroups, integer Carathéodory rank, knapsack polyhedra, and parametric integer optimization. His work bridges theoretical computer science, operations research, and discrete geometry, emphasizing structural properties of integer solutions and algorithmic efficiency. Professor Oertel's publication record (2013-2025) reveals consistent trends in theoretical integer programming, with recent work (2020-2025) concentrating on sparsity patterns, approximation in algebraic structures, and complexity bounds. He frequently collaborates with leading researchers including Iskander Aliev, Robert Weismantel, and Joseph Paat, publishing in top venues like Mathematical Programming and SIAM Journal on Optimization. His research demonstrates deep connections between combinatorial geometry and optimization theory.

• Energy Systems Modeling
• Geothermal Energy
• Optimization Algorithms
• +3 more

Smajil Halilovic is a Researcher at the Chair of Renewable and Sustainable Energy Systems at the Technical University of Munich . His work focuses on energy systems modeling and optimization, particularly for geothermal and renewable energy integration. Current projects include Geo.KW , a coupled hydrothermal and infrastructure model for urban-scale geothermal use Research emphasizes optimization techniques for groundwater heat pump systems Key contributions in PDE-constrained optimization and thermal resource assessment Halilovic has published extensively in Renewable Energy and Energy Conversion and Management , with recent work on spatial optimization of geothermal systems and urban heat planning. His collaborations with Prof. Thomas Hamacher and Kai Zosseder highlight his role in advancing sustainable energy infrastructure. He teaches Mathematical Modeling of Complex Systems in the Energy Field and contributes to interdisciplinary projects like the Interdisciplinary project internship: Concept development of a renewable energy system in a developing country . His publications demonstrate expertise in geothermal integration, optimization algorithms, and urban energy modeling.

• Combinatorial Optimization
• Algorithm Design
• Game Theory
• +3 more

Jannik Matuschke is an Associate Professor of Operations Management at the Department of Decision Sciences and Information Management, KU Leuven (Belgium). He holds affiliations with the KU Leuven Institute for Artificial Intelligence (Leuven.AI) and the KU Leuven Institute for Mobility (LIM). His research focuses on combinatorial optimization, algorithm design, game theory, robustness under uncertainty, and applications in logistics and production systems. Education: PhD in Mathematics (2013) from TU Berlin, advised by Martin Skutella and Britta Peis Postdoctoral positions at Universidad de Chile (2014) and TU München (2016–2018) DAAD P.R.I.M.E. fellowship at University of Rome 'Tor Vergata' (2015) Research Themes: Design of resilient infrastructures using multi-stage optimization Stochastic and robust project scheduling Applications in logistics network analytics and congestion modeling Recent Contributions: Recipient of the 2024 Meritorious Service Reward from Operations Research Journal Co-chair of WAOA 2025 and editorial roles at Omega, Operations Research Letters, and OR Spectrum Active in international workshops like FRICO 2025 and the Santiago Summer Workshop on Combinatorial Optimization Academic Leadership: Supervises 5 current PhD/postdoc researchers across logistics and optimization Coordinates the Master's Thesis program in Production and Logistics at KU Leuven Manages research projects on robust infrastructure design (2022–present) and stochastic scheduling (2020–present)

Christian KundeView profile

Researcher

• Process Optimization
• Global Optimization
• Surrogate Modeling
• +5 more

Dr.-Ing. Christian Kunde is a PostDoc researcher at the Max Planck Institute for Dynamics of Complex Technical Systems in the Process Synthesis and Process Dynamics group since 2021. Previously, he served as a PostDoc at the Chair for Automation/Modeling at Otto-von-Guericke University (OVGU) from 2017-2021, and as a Research Assistant and PhD Student at the same institution from 2009-2017. His educational background includes a Diploma in Systems Engineering and Engineering Cybernetics from OVGU (2004-2009) and a PhD dissertation titled "Global optimization in conceptual process design" completed in 2017. His formal engineering doctorate (Dr.-Ing.) reflects his specialized expertise in process systems engineering. Kunde's research focuses on physics- and data-based hybrid modeling for process optimization and state estimation, with particular emphasis on global optimization techniques and surrogate modeling. His work bridges theoretical computational methods with practical chemical engineering applications, addressing challenges in distillation, crystallization, and fuel cell systems. He has developed innovative approaches combining first-principles models with machine learning for improved process design and control. His publication record demonstrates consistent contributions to chemical process optimization, with recent work expanding into machine learning applications for Power-to-X processes and fuel cell modeling. The research shows a clear trajectory from fundamental optimization methods toward increasingly complex hybrid modeling approaches that integrate physics-based and data-driven techniques. Kunde actively contributes to academic knowledge dissemination through teaching, having instructed courses including Distributed Parameter Systems, State Estimation, and Systems and Control. His teaching portfolio reflects his research expertise in process dynamics and control systems, providing students with both theoretical foundations and practical applications in modern process engineering.

Armin NurkanovićView profile

Professor

• Optimal Control
• Numerical Methods
• Hybrid Dynamical Systems
• +4 more

Dr. Armin Nurkanović is an interim professor at the Technical University of Braunschweig's Department of Mathematical Optimization, where he teaches courses on dynamic optimization and numerical methods. Previously, he completed his PhD at the University of Freiburg under Prof. Moritz Diehl, focusing on optimal control of nonsmooth dynamical systems. His research emphasizes numerical methods for hybrid systems, real-time optimization, and applications in robotics and renewable energy systems. He has received the IEEE Control Systems Letters Outstanding Paper Award (2022) and was a finalist for the 2024 European Systems & Control PhD Thesis Award. Education: Bachelor's in Electrical Engineering (University of Tuzla, 2015) Master's in Electrical Engineering and Information Technology (Technical University of Munich, 2018) PhD in Control (University of Freiburg, 2023) Research Interests: Optimal control of hybrid and nonsmooth systems (e.g., Filippov systems, switched systems) Real-time optimization for model predictive control (MPC) Robust control theory and stochastic optimization Applications in robotics and renewable energy systems Teaching & Software: Developed open-source tools nosnoc and nosnoc_py for optimal control Teaching courses on numerical optimization and optimal control at TU Braunschweig Collaborations & Students: Open to academic and industry collaborations Supervises Bachelor's/Master's theses in mathematics, engineering, and computer science

• Combinatorial Optimization
• Graph Theory
• Operations Research
• +5 more

Cédric Costa is a Professor at the Conservatoire National des Arts et Métiers (CNAM), conducting research at the CEDRIC Laboratory. With a publication record spanning over 30 years from 1992 to 2023, he has established himself as a leading researcher in combinatorial optimization and operations research. His research interests focus on combinatorial optimization , graph theory , and operations research , with significant contributions to network design, discrete tomography, and robust optimization. His work bridges theoretical foundations with practical applications, particularly in renewable energy systems and telecommunications networks. Analysis of his publication trends shows a consistent focus on Steiner tree problems, matching theory, and robust network design, with increasing emphasis on renewable energy applications since 2015. His recent work (2020-2023) demonstrates expertise in wind farm optimization and robust network design under uncertainty. Prix Orange de l'Innovation 2012 (catégorie Réseau) for FTTH network deployment research Professor Costa has supervised numerous researchers including Poirion P-L, Bentz C, Ridremont T, Hertz A, Cotté G, and Picouleau C, as evidenced by their frequent co-authorship on publications. His research has been supported by projects related to wind farm optimization, optical network design, and robust energy systems. He has been actively involved in the ROADEF (French Operations Research Society) community, presenting regularly at their conferences.

Thorsten KochView profile

Professor

• Mathematical Optimization
• High-Performance Computing
• Artificial Intelligence
• +4 more

Thorsten Koch is a Professor for Software and Algorithms for Discrete Optimization at Technische Universität Berlin , with multiple leadership roles including Head of the Applied Algorithmic Intelligence Methods (A²IM) , Digital Data and Information for Society, Science, and Culture (D²IS²C) , Kooperativer Bibliotheksverbund Berlin-Brandenburg (KOBV) , and Forschungs- und Kompetenzzentrum Digitalisierung Berlin (digiS) . Based at Zuse Institute Berlin and affiliated with TU Berlin's Institute for Mathematics, he focuses on integrating mathematical optimization with high-performance computing and artificial intelligence to solve complex real-world problems. Research Pillars : Mathematical optimization algorithms Quantum computing applications AI/ML integration in decision systems Energy systems optimization Scientific software development Leadership Roles : Head of Applied Algorithmic Intelligence Methods (A²IM) Head of Digital Data & Information for Society, Science, and Culture (D²IS²C) Head of Kooperativer Bibliotheksverbund Berlin-Brandenburg (KOBV) Head of Forschungs- und Kompetenzzentrum Digitalisierung Berlin (digiS) Key Collaborations : Working with IBM Quantum on quantum optimization Collaborating across institutions for energy system modeling Developing open-source optimization tools like SCIP Contributing to digital library infrastructure Recent Research Trends : Quantum optimization benchmarking Machine learning-aided optimization Multi-objective decision frameworks Energy infrastructure optimization Adaptive algorithm design CO2 network modeling Impact : Advancing hybrid optimization methods Developing open-source tools for scientific computing Building digital infrastructures for libraries and research Exploring quantum-classical algorithm synergies