CV Tag: SimTech

  • Meta-Uncertainty in Bayesian Model Comparison

    Overview

    Scientific theories are often translated into statistical models and tested by comparing their predictions with real-world data. Bayesian model comparison can then estimate how much support each model receives.

    Led by Paul-Christian Bürkner at the University of Stuttgart’s SimTech Cluster of Excellence, this project addressed a further problem: the probabilities produced by Bayesian model comparison are themselves uncertain. This is known as meta-uncertainty, or uncertainty about uncertainty. The project ran from 2021 to 2024.

    Key results

    • Developed and evaluated methods for quantifying meta-uncertainty in Bayesian model comparison.
    • Used extensive model simulations as an additional source of information. These simulations help reveal hidden assumptions about how well the models represent possible real-world processes.
    • Distinguished between a “closed-world” setting, in which the true model is assumed to be among the models being compared, and an “open-world” setting, in which all candidate models may be incomplete or inaccurate.
    • Developed neural-network methods for amortized Bayesian inference. These methods perform much of the computational work in advance, making it faster to evaluate complex models on new data.
    • Created methods for detecting model misspecification—situations in which a model’s assumptions do not adequately describe the data.
    • Produced open-source software and workflows, including JANA and BayesFlow, to make simulation-based Bayesian analysis more efficient and accessible.

    Why it matters

    Bayesian model comparison can show which theories are better supported by available evidence, but its results depend on assumptions about the data, the models, and the possible alternatives. Quantifying meta-uncertainty makes these limitations more transparent.

    The project therefore contributes to more careful and reliable scientific inference. Its methods could improve prediction models in fields such as biology, medicine, climate science, and engineering, particularly when models are complex or computationally expensive.

    The results do not identify which theory is objectively true. Rather, they help researchers assess how reliable their model comparisons are and how much confidence they should place in the conclusions drawn from them.

    This project produced the following peer-reviewed publications: 

    • Schmitt M., Pratz V., Köthe U., Bürkner P. C., and Radev S. T. (2024). Consistency Models for Scalable and Fast Simulation-Based Inference. Proceedings of the Conference on Neural Information Processing Systems (NeurIPS). Available on ArXiv.
  • Beyond the Thesis – Das Leben nach dem Studium

    Beyond the Thesis – Das Leben nach dem Studium

     

    About the program: 

    This isn't your typical lecture series. It's an opportunity to engage in an open dialogue with others who've been or who are right where you are. Gain insights from those who've made these choices and are eager to share their experiences in academia and industry in a relaxed atmosphere. Hurry as space is limited to 30 participants!

    Three researchers in very different fields and roles are invited to debate and discuss, moderated by doctoral researcher Jonas Kneifl, the interesting topic of how to shape a career:

    • Inga Berre, Professor, Director VISTA Center for Modeling of Coupled Subsurface Dynamics, University of Bergen
    • Florian Leucht, Steering Wheel Hub Platform R&D Manager at Valeo 
    • Luiz Chamon, Independent Junior Research Group Leader on Information Systems (ELLIS-SimTech)

    You are also invited to mingle and connect over refreshments following the panel. 

    Join SimTech for an evening of enlightening conversations and invaluable networking opportunities as you chart your course toward a fulfilling career. The event will be held in English and is specifically not restricted to SimTech researchers and students.

     

    When? 

    May 15, 2024 at 18:00 CEST (discussions commencing at 18:30 CEST) 

     

    Where? 

    Entrepreneurship Café frei[Raum], Pfaffenwaldring 45, 

    University of Stuttgart, Campus Vaihingen 

     

    Interested?

    Please register here.

     

     

  • SimTech METEOR Competition 2024

    SimTech METEOR Competition 2024

    The SimTech METEOR competition was launched by the Knowledge Transfer Committee headed by Professor and SimTech scientist Jörg Fehr from the Institute of Engineering and Computational Mechanics at the University of Stuttgart.

    The competition stands out for its directness and efficiency by focusing on the rapid transfer of basic knowledge into the development of new technical products, materials and processes. A special feature is its rapid processing: in just three months after the submission deadline on February 23, 2024, the funding decisions will be announced in May 2024.

    The selection committee will decide which research questions and challenges will be supported with up to €100,000 each. This targeted funding not only enables the implementation of innovative ideas, but also combines them with an efficient use of funds.

    The SimTech METEOR competition is particularly relevant for companies and institutions, as it promotes the transfer and exchange between industry and research. The speed of knowledge transfer ensures that fundamental findings can flow directly into the development of new products, processes, and methods.

    Companies and institutions based in Germany are eligible to participate, regardless of their size. Preference is given to small companies and institutions based on a list of criteria.

    The SimTech METEOR competition focuses on the close integration of research and industry to tackle the challenges of the future together. The deadline for submissions is February 23, 2024. Join us and actively shape the future of technological innovation!

  • Cyber Valley scientist Steffen Staab appointed Fellow of ACM

    Cyber Valley scientist Steffen Staab appointed Fellow of ACM

    "I am delighted to have been honored with this title, which has only been awarded to a few German-speaking researchers to date," said Staab. The 54-year-old is researching ways in which computers can be made to solve complex problems through data and knowledge-based learning. "Up to now,” Staab explains, “artificial intelligence algorithms have been trained using datasets, which however, are often insufficient to comprehensively train an algorithm. This is why,” he continues, “my team and I are looking into ways to solve this problem by exploiting existing knowledge, which we have so far made insufficient use of." The computer scientist is pursuing this problem through interdisciplinary collaboration at the University of Stuttgart, within the "Data-integrated Simulation Science" (SimTech) Cluster of Excellence and in Cyber Valley, Europe's leading academic and industry consortium for AI research.

    Better synthetic fertilizer and cancer simulations

    Examples that illustrate Staab's research include artificial fertilizers and cancer. It would be ideal for both the agricultural sector and society in general if only the minimum amount of fertilizer were spread over a field to achieve the necessary concentration in the soil, which is not so easy because soil conditions vary greatly. Cancer is also subject to wide variations: whereas one tumor may grow in a branching form, another may remain compact, which has implications in terms of treatment. Researchers, such as Staab, are currently working at SimTech on the development of simulations that make such complex processes more predictable.

    As Staab explains: "We often don't know which part of the complex mathematical description – which is the starting point for a simulation – is of particular relevance when it comes to achieving meaningful results." Yet this would be important for the simulation. “This,” he continues, “is where AI algorithms can help but we need to train them on existing data, which is often rather scarce. If, however, we could also draw upon existing knowledge, the simulations could be carried out in a more stringent and realistic manner.” In many cases in fact, this knowledge is already available from laboratory studies carried out by other researchers – in some cases many years ago – on both synthetic fertilizers and tumors. "One could also refer to our approach as knowledge-infused simulation science," says Staab.

    About the Association for Computing Machinery

    The ACM is the world's largest and oldest scientific association for computer science. It comprises experts from the fields of education and research and aims to promote computer science both as a science and as a profession. The ACM also awards the Turing Prize, the highest honor in computer science. To be selected as an ACM Fellow, one's personal achievements in the fields studied by the ACM members must be outstanding.

    About Prof. Steffen Staab

    Steffen Staab was born in Würzburg in 1970 and studied Computer Science and Computer Linguistics at both the University of Erlangen-Nuremberg and the University of Pennsylvania. He received his doctorate in Computer Science from the University of Freiburg in 1998. He then went on to work at the University of Stuttgart’s Institute of Human Factors and Technology Management (IAT) as well as at the Fraunhofer Institute for Industrial Engineering (IAO), and later moved to the University of Karlsruhe (now known as KIT). In 2004, Staab was appointed Professor of Databases and Information Systems at the University of Koblenz and Landau, where he founded the Institute for Web Science and Technologies (WeST) in 2009. He has also held the Chair for Web and Computer Science at the University of Southampton since March 2015.

    Staab took up a Cyber Valley Professorship for Analytical Computing at the University of Stuttgart in February 2020. He founded the University's Institute for Artificial Intelligence where he serves as Managing Director and is also one of the two spokespersons for the SimTech Cluster of Excellence.

    Expert contact: 

    Prof. Steffen Staab, University of Stuttgart, Institute for Artificial Intelligence, Phone: +49 711 685-88100, E-Mail: steffen.staab@ki.uni-stuttgart.de

    Media contact:

    Lydia Lehmann, University of Stuttgart, University Communications, 
    Phone: +49 711 685-82297, E-Mail: lydia.lehmann@hkom.uni-stuttgart.de

  • Simulation science at the top

    Simulation science at the top

    An article from the university of Stuttgart, Interview: Lydia Lehmann. To the original text

    The halfway point of the funding period for the University of Stuttgart’s Data-integrated Simulation Science Cluster of Excellence was recently reached. What have you achieved so far?

    Prof. Wolfgang Nowak: Conventional simulation technology involves the numerical simulation of physical principles on computers. Huge amounts of experimental data have recently become available that can improve such simulations. This is why we had set ourselves the goal of bringing about a paradigm shift away from traditional simulation technology towards "data-integrated simulation science"…

    Prof. Thomas Ertl: …yes, we incorporated data science and machine learning with a view to merging simulation science and data science to establish a more advanced simulation discipline.

    Nowak: That was a really radical change and what we now know is that this paradigm shift has played a central role in all of the 80 projects we have successfully completed during the first half of the project. Comments from our researchers, such as: “I am among the foremost researchers doing data-integrated simulation science in my field”, which we hear again and again, show that it has been successful both at the collective and personal levels.

    Ertl: The heterogeneity and breadth of SimTech's research successes can be seen in all seven project networks; focusing on individual ones is almost pointless. All in all, simulation science has coalesced and matured over the past 15 years from isolated approaches into an independent discipline.

    We run our own SimTech bachelor's and master's study programs, host our own graduate school where more than 200 people have earned doctorates in simulation science, and are proud to have ten post-doctoral habilitations in simulation science. There is a funding structure in place for post-doc projects, and we have established seven junior research groups: we have been joined by four men and three women, mostly from outside the university, who are now leading their research groups with great success. We also announced six new professorships, all of which have since been taken up by excellent researchers. The fact that SimTech is an internationally established trademark of the University of Stuttgart is a unique selling point.

    Director of the Stuttgart Centre for Simulation Science and co-spokesperson of the Cluster of Excellence SimTech Prof. Ertl (left) with co-spokesperson Prof. Nowak. Photo: Jakob Dürrwächter, Daniel Kempf und Claus-Dieter Munz, Universität Stuttgart
     

    What do you personally like most about SimTech?

    Ertl: Even as a veteran of almost 20 years, what fascinates me is the level of collaboration and personal interaction – and the fact that we have been so successful on this basis. The development over the years of such an excellent culture of collaboration in a cluster of this size, which still leaves room for many idiosyncrasies and in which everyone is on an equal footing, is not something that can be taken for granted. I'm saying that now as the spokesperson, but this is the lived reality across all status groups.

    Nowak: Yes – how did one of the reviewers put it during our last review? Ah yes: "This degree of team spirit cannot be faked."

    Ertl: (laughs.) Yes, that was a phrase from someone from outside the group that sums it up nicely.

    Prof. Steffen Staab: What always impresses me are our annual SimTech status meetings in Bad Boll, which are attended by around 200 active SimTech researchers. That's where you get a physical sense of the size of the undertaking as you walk the corridors and how enthusiastic everyone is about it. 

    Nowak: I like the interdisciplinarity on an equal footing and having our own SimTech vocabulary. As an environmental engineer and porous-media researcher, I stroll into the math building, and after enough discussions with mathematicians, I no longer feel out of my depth. Then I head over to the computer science building with new questions and experience the same thing. (laughs.) And I sit on the board of directors alongside a theoretical chemist, a systems biologist and others, where discussions also take place on various professional levels. I love that!

    So SimTech is the living embodiment of the "Stuttgarter Weg" (Stuttgart way) of doing interdisciplinary research. And, in keeping with the University of Stuttgart's vision, why is your simulation research important for society and for the future?

    Ertl: Simulations are run by people and don’t take place in a vacuum. We want to make simulations and the data they yield available for different application contexts – think "pervasive simulation and visualization": not only on a supercomputer or at a lab desk, but also for a geologist taking pictures on a tablet with built-in sensors outside in the real world, collecting data and feeding it directly into a simulation that then runs remotely on a supercomputer before feeding the results back to the user. The issues involved include the interfaces between supercomputers and mobile devices as well as more compact displays for tablets.

    Nowak: The Digital Human Model is another example, where our researchers are working on personalized prognoses on a per-patient basis. A patient with a spinal disk injury is admitted to the hospital to have a prosthesis implanted. We need to quickly calculate the exact forces acting on the bones and spinal disk due to nerve stimuli, muscles, and tendons in this specific patient. A simulation can then be used to predict which implant would be the best.

    Is this just a distant dream for the future, or are we on the verge of using such personalized spinal disk implants?

    Nowak: We at SimTech are basic researchers; the systems we program are not immediately used in hospitals. There is one more technology transfer stage in between. However, we can already use real CT data to show that the necessary calculations can actually be done. And we can use augmented or virtual reality glasses to visualize the force conditions that occur during movements as well as which part of the muscle they affect and how.

    Staab: There are several transfer-oriented application projects that are not funded by the Cluster of Excellence, but which build on SimTech's methodological innovations. For example, one project with the University Hospital of Jena is just getting underway, which involves simulating processes in the liver and simultaneously using machine learning to support the tumor board, in which doctors with expertise in various specialist areas consult with each other on the diagnosis and treatment of liver tumors. Incidentally, the industrial sector is already using SimTech's methodological findings as the result of transfer activities of this kind.

     

    What will change at SimTech in the second half of the funding period? What would you like to focus on?

    Ertl: I will be retiring in October. We started the SimTech generation change last year, since which time Wolfgang Nowak and Steffen Staab have been co-spokespersons for the cluster, my successor duo. Holger Steeb will be assuming the position of director at the Stuttgart Center for Simulation Science. With these three individuals, an excellent team is in place to prepare and hopefully lead SimTech's next application for excellence status to success.

    Staab: We’re aiming for a combination of evolution and revolution. Whilst data-integrated simulation science does indeed represent a new paradigm, we will of course continue to think beyond it. To what extent will simulation science undergo a fundamental change? That's a challenge because there is only one simulation science, yet many contributing disciplines all with their own different vernaculars. I believe that achieving success here will enable us to make a considerable difference, because we will have an impact on various disciplines.

    Nowak: The opportunity is already there to pick up on certain topics right now in order to redirect SimTech's focus for potential follow-up funding as a cluster of excellence. One such area of interest is our new "Quantum Computing for Simulation" project network, where we are working closely with theoretical chemists as well as theoretical and experimental physicists to make quantum computers fit for our simulation runs and to tune the algorithms accordingly.

    Staab: The second thing we want to focus on is "Knowledge-Infused Simulation Science," or knowledge integration. We want to make simulation systems as intelligent as possible, for example by having them learn from experience or by incorporating expert knowledge into our simulations, which would save computing power and time.

    Nowak: And we are aware of what the science-supporting structures have accomplished so far – for example, when it comes to promoting young scientists, internationalization, diversity, study programs, the management of scientific data, public relations, and reflecting on one's own work. We would like to continue our success and surpass what we have achieved so far.

    About SimTech

    From 2007 to 2018, there was the Cluster of Excellence 310 Simulation Technology at the University of Stuttgart, which was funded twice by the DFG. Since 2019, there has been the Cluster EXC 2075 Data-integrated Simulation Science, which is currently in its first funding phase and is now eligible to apply for a second funding phase, as part of the Excellence Strategy of the German federal and state governments. Both clusters are internationally established under the acronym SimTech. In addition to the clusters, there is the Stuttgart Centre for Simulation Science, a faculty-like institution at the University of Stuttgart that ensures the sustainability of simulation research independently of the clusters of excellence.

    SimTech is not assigned to any faculty, but seven faculties are involved in SimTech. It is an independent profile area of the University of Stuttgart. In terms of subject matter, SimTech is a mixture of engineering sciences, related natural sciences up to theoretical chemistry as well as mathematics and computer science. The focus in the applications that SimTech serves is on engineering. The researchers work in a method-oriented manner. They research fundamental simulation methods that are universally applicable.

    The interdisciplinarity and relevance of SimTech can be seen, for example, in the connections to the AI research consortium Cyber Valley, to the network European Laboratory for Learning and Intelligent Systems (ELLIS) or to the Stuttgart Research Focus Interchange Forum for Reflecting on Intelligent Systems (IRIS) at the University of Stuttgart.

  • SimTech2023

    SimTech2023

     

    Vom 4. bis 6. Oktober 2023 laden die Universität Stuttgart und der Exzellenzcluster EXC 20275 "Data-integrated Simulation Science" (SimTech) führende Forscher im Bereich der datenintegrierten Simulationswissenschaft zur SimTech2023-Konferenz nach Stuttgart ein. Diese neue Art der Simulation integriert verschiedene Arten von Daten, von experimentellen Daten über Simulationsergebnisse bis hin zu Metadaten, um die Effizienz und Zuverlässigkeit von Simulationen zu verbessern.

     

    Conference Webpage

     

    Topics of the conference

    The conference will cover topics such as

    • classical computational science enhanced by learning based components,
    • data-based scale bridging,
    • high performance computing,
    • numerical solvers optimized by meta-data based methods,
    • machine learning for simulation and visualization,
    • data-based (surrogate) modeling for interactive applications and control problems,
    • Bayesian approaches for uncertainty reduction and model assessment and many more.

     

    Is this conference for you?

    The conference is intended to be a major platform for promoting exchange amongst representatives of the international research community in these interdisciplinary fields, bringing together mathematicians and life scientists, engineers and chemists, computer scientists and sociologists, physicists and philosophers. In keynote lectures, renowned scientists present the latest ideas at the interface between data and simulation. With this multi-disciplinary environment, we continue to advance towards our goal at the Stuttgart Cluster of Excellence SimTech: From Multi-X Simulations and Data-Driven Approaches to Data-Integrated Simulation Science.

    We are looking forward to welcome you all in Stuttgart!

     

    Organizer:

    Professor Miriam Schulte

  • Call for posters: SimTech2023

    Call for Posters for SimTech2023
     

    The University of Stuttgart and the Cluster of Excellence EXC 20275 "Data-integrated Simulation Science" (SimTech) extended the deadline for poster abstract submission for SimTech2023 to April 7, 2023. 

    They invite authors to submit short abstracts for poster presentations of new ideas, work in progress and promising new results in all areas of data-integrated simulation – from (hybrid) models for biological, environmental or technical systems and material over numerical methods for partial differential equations and learning, stochastic (Bayes) approaches for uncertainty quantification, reduction and optimization, dynamical systems and control to high performance computing for hybrid and novel architectures including quantum computing.

    One or more authors of accepted posters are expected to present the poster in person at the conference. Accepted posters and the corresponding short abstracts will be published online via the conference website.

    Please submit your abstract as a single pdf file to conference_ps@simtech.uni-stuttgart.de using the latex template attached here.

    Fell free to forward this call to interested colleagues! We are looking forward to welcoming you in Stuttgart.

    Timeline

    • Deadline for poster abstract submission: April 7
    • Notification of acceptance: April 30
    • Early Registration Deadline: May 15.

    The SimTech2023 Conference

    Most parts of the presentation program including keynote speakers are published at the conference webpage: www.simtech2023.uni-stuttgart.de

    The conference will cover topics such as

    • classical computational science enhanced by learning based components, 
    • data-based scale bridging, 
    • high performance computing, 
    • numerical solvers optimized by meta-data based methods,
    • machine learning for simulation and visualization, 
    • data-based (surrogate) modeling for interactive applications and control problems, 
    • Bayesian approaches for uncertainty reduction and model assessment and many more. 
  • Research Talk: Semantic Probabilistic Layers for Neuro-Symbolic Learning

    Extending current deep learning architectures to correctly conform to prior knowledge and capture symbolic constraints while guaranteeing their satisfaction is a big open problem. Doing so would benefit many structured-output prediction (SOP) tasks in machine learning such as predicting rankings, hierarchies and other structured objects. In this talk, I will discuss how we can design a predictive layer for SOP that guarantees its predictions to be consistent with a set of predefined symbolic constraints. This Semantic Probabilistic Layer (SPL) can act as a drop-in replacement of existing predictive layers while still being amenable to end-to-end learning via exact maximum likelihood. SPLs combine exact probabilistic inference with logical reasoning in a clean and modular way via probabilistic circuits. We empirically demonstrate that SPLs outperform other neuro-symbolic approaches in terms of accuracy on challenging SOP tasks including hierarchical multi-label classification, pathfinding and preference learning, while retaining perfect constraint satisfaction.

     

    Antonio Vergari is a Lecturer (Assistant Professor) in Machine Learning at the University of Edinburgh. His research focuses on efficient and reliable machine learning in the wild; tractable probabilistic modeling and combining learning with complex reasoning. He recently was awarded an ERC Starting Grant on automating probabilistic reasoning for trustworthy ML. Previously he was postdoc in the StarAI Lab lead by Guy Van den Broeck at UCLA. Before that he did a postdoc at the Max Planck Institute for Intelligent Systems in Tuebingen in the Empirical Inference Department of Bernhard Schoelkopf. He obtained a PhD in Computer Science and Mathematics at the University of Bari, Italy. He likes to tease and challenge the probabilistic ML community at large on how we desperately need reliable ML an AI models nowadays. To this extent, he organized a series of tutorials, workshops, seminars and events at top ML and AI venues such as UAI, ICML, AAAI, IJCAI and NeurIPS and last year a Dagstuhl Seminar.

  • Elisabeth Schiemann Kolleg welcomes Maria Wirzberger

    Elisabeth Schiemann Kolleg welcomes Maria Wirzberger

    “Being accepted into the program allows me to sharpen my scientific profile in a more technically oriented direction and to expand my professional network in disciplinary terms,” says Wirzberger. She had already taken this path in 2018 as a post-doctoral researcher by moving to the Max Planck Institute for Intelligent Systems in Tübingen. There, the cognitive psychologist led the development of AI-supported software for training attentional control. In 2020, she moved to the University of Stuttgart, where Wirzberger heads the Department of Teaching and Learning with Intelligent Systems as well as serving as spokesperson for the Interchange Forum for Reflecting on Intelligent Systems (IRIS) and is in charge of the “Platform of Reflection” of the Data-Integrated Simulation Science (SimTech) Cluster of Excellence.

    Networking the disciplines

    Interdisciplinary networking has always played an important role for Wirzberger. Already in her master’s studies at the Technical University of Berlin, she decided against a classic master’s program in psychology with “Human Factors”. “In my bachelor’s thesis, I realized how much I enjoy programming and how exciting issues at the interface between humans and technology are. That’s why I wanted to develop further in this direction,” says Wirzberger. As a doctoral student in the DFG Research Training Group “CrossWorlds” at Chemnitz University of Technology, she worked closely with colleagues from the faculty of Computer Science.

    Mentoring as an important building block

    When it comes to networking, mentoring also plays an important role for Wirzberger. Particularly in cross-disciplinary mentoring programs, she sees the opportunity to benefit from the exchange with established colleagues and thereby also develop further in her role as a leader.

    For Wirzberger, admission to the Elisabeth Schiemann Kolleg is therefore an important step in strategically advancing both her own professionalization and her vision for the development of adaptive educational technologies.

    Contact

    Jun.-Prof. Dr. Maria Wirzberger
    Interchange Forum for Reflecting on Intelligent Systems
    maria.wirzberger@iris.uni-stuttgart.de
    https://www.iris.uni-stuttgart.de/

  • Wissensbasiertes maschinelles Lernen und seine Anwendungen

    Wissensbasiertes maschinelles Lernen und seine Anwendungen

    Trotz der bemerkenswerten Fortschritte, die in den letzten Jahrzehnten bei den Techniken des maschinellen Lernens (ML) und bei datengesteuerten Verfahren der künstlichen Intelligenz allgemein erzielt wurden, sind die meisten ML-Ansätze derzeit nicht in der Lage, interpretierbare Informationen und Wissen aus Daten zu extrahieren. Außerdem können Vorhersagen, die aus rein datengesteuerten Modellen gewonnen werden, physikalisch inkonsistent oder sogar unplausibel sein. Derzeit wird intensiv untersucht, wie man vorhandenes (Experten-)Wissen in das ML-Design einbeziehen kann, um diesen Herausforderungen zu begegnen. Die sorgfältige Integration von Vorwissen über einen Prozess, wie z. B. physikalische oder chemische Erkenntnisse, in das Lernverfahren verbessert nicht nur die Qualität der gelernten Repräsentation, sondern beschleunigt auch den Lernprozess mit weniger Datenproben.

    Diese Summer School zielt darauf ab, den Teilnehmenden ein grundlegendes Verständnis dafür zu vermitteln, wie verschiedene Aspekte des Wissens inkl. etablierter Simulationsparadigmen in moderne Lernarchitekturen eingebracht werden können.

    Confirmed speakers

    Zielgruppe

    Die SimTech Summer School richtet sich in erster Linie an Masterstudierende, Promovierende und early PostDocs.