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Prof. Dr. rer. nat. habil. Martin O. Steinhauser

Professor of Applied Physics and Computer Science

Prof. Dr. rer. nat. habil.
Martin Oliver Steinhauser
Professor of Applied Physics and Computer Science
Building 8, Room 115
Fax : +49 69 1533-2206

Office Hours (Sprechzeiten)

By appointment via email

Profiles and publication lists:

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    Academic and Research Profile

    Martin O. Steinhauser is Professor of Applied Physics and Computer Science at Frankfurt University of Applied Sciences. His academic work connects applied physics, computational modelling, scientific simulation, and research-based teaching, with a strong emphasis on scientific synthesis across physics, mathematics, and computer science.

      Selected Academic Highlights

      • Habilitation in Physical Chemistry at the University of Basel, with venia legendi and venia docendi; appointed Privatdozent.

      • Author of scientific textbooks and monographs published by Springer and De Gruyter.

      • More than 40 peer-reviewed publications and more than 50 invited presentations.

      • Principal investigator and project leader in externally funded research projects exceeding €9 million.

      • Research and teaching experience at leading universities and research institutions, including the Max Planck Institute for Polymer Research, the German Cancer Research Center (DKFZ), the University of Oxford, the University of Tokyo, Nanyang Technological University (NTU) in Singapore, and the University of Basel in Switzerland.

      • Research profile spanning applied physics, computational science, materials modelling, soft matter, and scientific simulation.

      The scientific work of Prof. Dr. habil. M. O. Steinhauser focuses on multiscale modeling in computational physics, ranging from quantum-theoretical and molecular descriptions to microscopic and macroscopic material behavior. His research connects mathematical modeling, numerical simulation, and experimental validation, with applications in soft matter, biological cell systems, shock-wave physics, material failure, hypervelocity impact, and space-debris impact.

      His research uses numerical and theoretical tools that connect different physical scales. Molecular dynamics, Monte Carlo methods, and coarse-grained simulation are used for molecular and mesoscopic systems in statistical physics. The discrete element method and smoothed particle hydrodynamics are used for particulate and highly dynamic systems. Finite element methods and continuum-based modeling are used for macroscopic material behavior. Selected aspects of quantum theory and electronic-structure theory are included where the microscopic origin of material properties is relevant.

      A distinctive part of his research profile is the combination of computational research with substantial experimental experience. This includes work with living tumor cells under sterile laboratory conditions, optical microscopy, electron microscopy, atomic force microscopy (AFM), fluorescence microscopy, ultrasound microscopy, nondestructive testing, and high-speed camera systems for shock-wave, impact, and high-strain-rate phenomena.

      In the context of tumor-cell research, he developed a laser-induced shock-wave approach for investigating physical mechanisms of tumor-cell damage and destruction and established the corresponding laser laboratory. This work was published in the Nature Portfolio journal Scientific Reports.

      His academic work also includes successful externally funded research projects as pricipal investigator, especially in the Fraunhofer context, as well as research-based teaching, advanced textbook and monograph projects, and the supervision of bachelor’s theses, master’s theses, doctoral research projects, and individual research projects.

      Books, Monographs, and Selected Publications

      Martin O. Steinhauser
      Computational Multiscale Modeling of Fluids and Solids - Theory and Applications
      Springer, Heidelberg, Berlin, 2008

      Martin O. Steinhauser
      Computer Simulation in Physics and Engineering
      de Gruyter, Leipzig, Berlin, Boston, 2013

      Martin O. Steinhauser
      Quantenmechanik für Naturwissenschaftler
      Springer, 2. Auflage, Heidelberg, Berlin, 2017

      Martin O. Steinhauser
      Multiscale Modeling and Simulation of Shock Wave-Induced Failure in Materials Science
      Springer, Heidelberg, Berlin, 2018

      Martin O. Steinhauser
      Computational Multiscale Modeling of Fluids and Solids - Theory and Applications
      3rd revised and expanded edition, Springer Spektrum Schweiz, Basel, 2017

      Martin O. Steinhauser
      Quantenmechanik für Naturwissenschaftler
      Springer, 2. Auflage, Heidelberg, Berlin, 2022

      Martin O. Steinhauser
      Computational Multiscale Modeling of Fluids and Solids - Theory and Applications
      Springer, Heidelberg, Berlin, 2022

       

      Marco Krummenacher and Martin O. Steinhauser

      Self-assembly and complex formation of amphiphilic star and bottle-brush block copolymers
      J. Chem. Phys, 157, 154904 (2022)

      Martin O. Steinhauser
      Multiscale modeling, coarse-graining and shock wave computer simulations in materials science
      AIMS Materials Science, 4, 6, 1319-1357 (2017) doi: 10.3934/matersci.2017.6.1319

      Martin O. Steinhauser
      Novel Computer Simulations Adressing the Impact Risks in Space from Orbiting Debris
      Mater. Sci. Eng., 1, 1005, 1-5 (2017)

      Erkai Watson, Martin O. Steinhauser
      Discrete Particle Method for Simulating Hypervelocity Impact Phenomena
      Materials, 4, 379, 1-22 (2017)

      Katja Schladitz, Andreas Büter, Michael Godehardt, Oliver Wirjadi, Johanna Fleckenstein, Tobias Gerster, Ulf Hassler, Katrin Jaschek, Michael Maisl, Ute Maisl, Stefan Mohr, Udo Netzelmann, Tobias Potyra, Martin O. Steinhauser
      Non-destructive characterization of fiber orientation in reinforced SMC as input for simulation based design
      Composite Structures 160, 195-203 (2017)

      Martin Oliver Steinhauser, Tanja Schindler
      Particle-based Simulations of bilayer membranes: Self-assembly, structural analysis, and shock wave damage
      Comput. Part. Mech. 4, 69-86, (2017) doi:10.1007/s40571-016-0126-3

      Martin Oliver Steinhauser
      On the Destruction of Cancer Cells Using Laser-Induced Shock Waves: A Review on Experiments and Multiscale Computer Simulations
      Radiol. Open J. 1, 60-75 (2016) doi:10.17140/ROJ-1-110

      Tanja Schindler, D. Kröner, Martin O. Steinhauser
      On the dynamics of molecular self-assembly and the structural analysis of bilayer membranes using coarse-grained molecular dynamics simulations
      Biochimica et Biophysica Acta (BBA)-Biomembranes 1858, 1955-1963 (2016)

      Martin Oliver Steinhauser
      Discrete Particle Methods for Simulating High-Velocity Impact Phenomena
      in: G. R. Liu and Shaofan Li (eds.): Proceedings of the International Conference on Computational Methods (ICCM 2016), 3, 915-923 (2016)

      Martin Oliver Steinhauser, Mischa Schmidt
      Destruction of Cancer Cells by Laser-Induced Shock Waves: Recent Developments in Experimental Treatments and Multiscale Computer Simulations
      Soft Matter 10, 4778-4788 (2014)

      Mischa Schmidt, Ulf Kahlert, Johanna Wessoleck, Donata Maciaczyk, Benjamin Merkt, Jaroslaw Macziaczyk, Jens Osterholz, Guido Nikkhah and Martin O. Steinhauser: 
      Characterization of a Setup to test the Impact of High-Amplitude Pressure Waves on Living Cells
      Sci. Rep. 4, 3849 (2014)

      M. O. Steinhauser: 
      Introduction to Molecular Dynamics Simulations: Applications in Hard and Soft Condensed Matter Physics
      in Prof. Lichang Wang (ed): Molecular Dynamics - Studies of Synthetic and Biological Macromolecules, InTech (2012), DOI: 10.5772/36289, ISBN: 978-953-51-0444-5, Available from:  https://www.intechopen.com/books/molecular-dynamics-studies-of-synthetic-and-biological-macromolecules

      M. O. Steinhauser
      Modeling Dynamic Failure Behavior in Granular and Biological Materials: Emerging New Applications 
      Cover page article in Y. M. Haddad (ed): AES Technical Reviews, International Journal Part C, IJATEMA 1, 1-19 (2012), ISSN: 1916-5366

      G. C. Ganzenmüller, S. Hiermaier and M. O. Steinhauser
      Energy-Based Coupling of Smooth Particle Hydrodynamics and Molecular Dynamics
      Eur. Phys. J. Special Topics 206, 51-60 (2012)

      G. C. Ganzenmüller, S. Hiermaier and M. O. Steinhauser
      Consistent Temperature Coupling with Thermal Fluctuations of Smooth Particle Hydrodynamics and Molecular Dynamics
      PLoS ONE 7, e51989 (2012)

      G. C. Ganzenmüller, S. Hiermaier and M. O. Steinhauser
      Multiscale Modeling and Simulation of Shock-Wave Impact Failure in Hard and Soft Matter 
      in K. Kontis (ed): Shock Waves, Springer Verlag, Heidelberg (2012), ISBN 978-3-642-25687-5

      G. C. Ganzenmüller, S. Hiermaier and M. O. Steinhauser
      Shock-Wave Induced Damage in Lipid Bilayers: A Dissipative Particle Dynamics Simulation Study
      Soft Matter 7, 4307 (2011)

      M. O. Steinhauser, J. Schneider and A. Blumen
      Simulating Dynamic Crossover Behavior of Semiflexible Linear Polymers in Solution and in the Melt
      J. Chem. Phys. 130, 164902 (2009)

      M. O. Steinhauser and S. Hiermaier
      A Review of Computational Methods in Materials Science: Examples from Shock-Wave and Polymer Physics
      Int. J. Mol. Sci. 10, 5135 (2009)

      M. O. Steinhauser, K. Grass, E. Strassburger and A. Blumen
      Impact Failure of Granular Materials -- Non-Equilibrium Multiscale Simulations and High-Speed Experiments
      Int. J. Plasticity 25, 161 (2009)

      Martin O. Steinhauser 
      Failure of Granular Materials under Impact: Modeling and Multiscale Simulations 
      in E. Oñate and D. R. J. Owen (ed): Particles 2009, CIMNE, Barcelona (2009)

      M. O. Steinhauser
      Static and Dynamic Scaling of Semiflexible Polymer Chains -- A Molecular Dynamics Simulation Study of Single Chains and Melts
      Mech. Time-Depend. Mater. 12, 1385 (2008)

      M. Kühn and M. O. Steinhauser
      Modeling and Simulation of Microstructures using Power Diagrams: Proof of Concept
      Appl. Phys. Lett. 93, 034102 (2008)

      M. O. Steinhauser, J. Schneider and A. Blumen
      Equilibrium and Non-Equilibrium Molecular Dynamics Simulations of Flexible and Semiflexible Polymer Melts 
      in R. B. Hall, H. Lu and H. J. Qi (eds): Mechanics of Time-Dependent Materials, Current Associates, 57, Moorehouse Lane, Red Hook, NY, USA pp. 12571 (2008)

      M. O. Steinhauser and M. Kühn
      The Use of Optimized Power Diagrams for Mesoscopic Shock Wave Modeling 
      in A. Khan, B. Farrokh (eds): Plasticity of Conventional and Emerging Materials: Theory and Applications, Neat Press, Cleveland, Ohio, USA pp. 322 (2007)

      M. O. Steinhauser
      Computational Methods in Polymer Physics 
      Chapter in Recent Res. Devel. Physics 7, 59 (2006), ISBN 81-7895-237-8

      M. O. Steinhauser, K. Grass, K. Thoma and A. Blumen
      Impact Dynamics and Failure of Brittle Solid States by Means of Nonequilibrium Molecular Dynamics Simulations
      Europhys. Lett. 73, 62 (2006)

      M. O. Steinhauser and M. Kühn
      Numerical Simulation of Fracture and Failure Dynamics in Brittle Solids 
      in A. Khan, S. Kohei and R. Amir (eds): Anisotropy, Texture, Dislocations, Multiscale Modeling in Finite Plasticity and Viscoplasticity and Metal Forming, Neat Press, Maryland, USA pp. 634 (2006)

      M. O. Steinhauser and M. Kühn
      Modeling of Shock-Wave Failure in Brittle Materials  
      in P. Gumbsch (ed): MMM Multiscale Materials Modeling, IRB Publishing, Stuttgart, Germany pp. 380 (2006)

      M. Kschischo, R. Kern, C. Gieger, M. O. Steinhauser and R. Tolle
      Automatic Scoring and Quality Assessment using Accuracy Bounds for FP-TDI SNP Genotyping Data
      Applied Bioinformatics 4, 75 (2005)

      M. O. Steinhauser and K. Grass 
      Failure and Plasticity Models of Ceramics. A Numerical Study 
      in A. Khan, S. Kohei and R. Amir (eds): Dislocations, Plasticity, Damage and Metal Forming, Materials Response and Multiscale Modeling, Neat Press, Maryland USA, pp. 370 (2005)

      M. O. Steinhauser
      A Molecular Dynamics Study on Universal Properties of Polymer Chains in Different Solvent Qualities. Part I. A Review of Linear Chain Properties
      J. Chem. Phys. 122, 094901 (2005)

      K. Grass, A. Blumen, M. O. Steinhauser and K. Thoma 
      Sequential Modeling of Failure Behavior in Cohesive Brittle Materials 
      in R. Garcia-Roja, H.-J. Herrmann, S. McNamara (eds): Powders and Grains, A. A. Balkema Publishers, Leiden, pp. 1447 (2005) 

      M. O. Steinhauser und K. Thoma 
      MMM-Tools: Multiskalen Modellierung und -Simulation 
      in Congress Intelligente Leichtbausysteme (ILS), Vogel Industrie Medien, Wolfsburg, pp. 56 (2004)

      B. Dünweg, D. Reith, M. O. Steinhauser and K. Kremer 
      Corrections to Scaling in the Hydrodynamic Properties of Dilute Polymer Solutions
      J. Chem. Phys. 117, 914 (2002)

      R. G. Winkler, M. O. Steinhauser and P. Reineker
      Complex Formation in Systems of Oppositely Charged Polyelectrolytes: A Molecular Dynamics Simulation Study
      Phys. Rev. E 66, 021802 (2002)

      Teaching, Books, and Academic Synthesis

      Martin O. Steinhauser’s current academic work places strong emphasis on research-based teaching and the synthesis of scientific concepts in textbooks, monographs, and structured teaching materials. His teaching covers physics, mathematics, scientific computing, numerical simulation, mechanics, calculus, discrete mathematics, quantum theory, and related areas of theoretical and computational science. This work connects mathematical clarity, physical intuition, computational methods, and conceptual understanding.

      Degrees and Education

      Martin O. Steinhauser studied physics and mathematics at the universities of Heidelberg, Ulm, and Munich, and at the University of Massachusetts Amherst.

      • In 1998, he received the degree Diplom-Physiker from the University of Ulm.
      • In 2001, he completed his PhD in physics at the Max Planck Institute for Polymer Research, where he worked with Kurt Kremer and Kurt Binder in association with Johannes Gutenberg University Mainz.
      • In 2018, he completed his habilitation in Physical Chemistry at the University of Basel and was awarded the venia legendi and venia docendi.

      Selected Academic Indicators

      • Recipient of a Fraunhofer EMI FastTrack Research Award in 2019.

      • Visiting Professor in Japan, with English-language academic teaching in Tokyo.

      • Research visits and invited academic activities at the University of Oxford.

      • More than 25 years of research experience in physics, computational science, scientific simulation, biomedical research, and externally funded applied research.

      • Formal higher-education teaching training, including a SEDA-accredited qualification in Supporting Learning.

      • Shortlisted among the final six candidates for the University of Basel Teaching Award in 2018.

      • Professional experience in software industry and private-sector biomedical and pharmaceutical research, including drug discovery and computational approaches to biological and medical questions.

      Major Research Areas

      Computational and Mathematical Physics
      Multiscale materials modelling, molecular simulation, numerical methods, high-performance scientific computing.

      Materials, Soft Matter, and Biophysical Systems
      Polymers, biomembranes, genetic epidemiology, tumor biophysics, mechanobiology, laser-induced shock-wave effects.

      Foundations, Teaching, and Scientific Synthesis
      Quantum theory, spacetime geometry, textbook and monograph projects.

      The research areas presented below document selected previous research projects and scientific themes from Prof. Steinhauser’s academic work in applied physics, computational modeling, shock-wave physics, soft matter, materials science, and biological systems. They provide an overview of the experimental, theoretical, and computational methods that have shaped his scientific background. At Frankfurt University of Applied Sciences, this experience is increasingly integrated into research-based teaching, scientific synthesis, and the preparation of advanced textbooks in physics, mathematics, and computational science.

      Teaching Videos and Scientific Presentations

      Selected academic videos are available on the personal YouTube channel linked below. The channel contains selected teaching videos, lecture material, and scientific presentations in mathematics, physics, scientific computing, and related areas. It is maintained as a personal academic channel and is not an official communication channel of Frankfurt University of Applied Sciences.

       YouTube channel: Prof. Dr. habil. M. Steinhauser  

      Research Topics for Theses, Doctoral, and Individual Projects

      I offer research-oriented topics for bachelor’s theses, master’s theses, doctoral research projects, and individual research projects in computational science, computational physics, mathematical modelling, and related areas.

      The scope, mathematical depth, implementation effort, and expected scientific originality are adjusted to the intended level of the project. Bachelor’s theses usually focus on a clearly defined task; master’s theses require a higher degree of independent work, methodological understanding, and scientific evaluation; doctoral research projects require a substantially deeper theoretical, computational, and scientific contribution.

      Individual research projects are possible if they are clearly related to my research areas and if the student has sufficient prior knowledge for the proposed topic. Such projects require the same initial information as theses: a CV, a transcript or overview of completed courses and credits, relevant technical or mathematical skills, and a concrete indication of the intended project direction.

      Possible thesis, doctoral, and individual research topics may be available in the following areas:

      • Molecular dynamics and coarse-grained simulation of biomembranes and soft matter systems
      • Polymer physics, polymer networks, and mechanically loaded molecular systems
      • Simulation of polyelectrolyte complex formation
      • Interaction of macromolecules with biomembranes and vesicles
      • Modeling and simulation of polymer networks, crosslinked systems, and cytoskeleton-like structures
      • Static and dynamic properties of hyperbranched polymers and fractal structures
      • Shock-wave physics, impact processes, and materials under mechanical loading
      • Modeling and simulation of space-debris impact on satellite structures
      • Simulation of material failure, including possible machine-learning approaches
      • Numerical method development for particle-based and continuum-based simulations
      • Development of efficient, optimized, and parallelized molecular dynamics simulation codes
      • Parallel computing, scientific software development, and high-performance simulation algorithms
      • Data analysis, visualization, and evaluation of simulation results in computational physics
      • Selected topics in mathematical physics and foundations of quantum theory, depending on the student’s prior preparation and background
      • Other topics by individual agreement, provided that they fit my research profile and have sufficient scientific merit

      If you are interested in doing a bachelor’s thesis, master’s thesis, doctoral research project, or individual research project with me, please read this page carefully first and then send me an email with the following information:

      • an acknowledgement that you have carefully read this whole page and the pages linked or branching from it;
      • the intended type of project: bachelor’s thesis, master’s thesis, doctoral research project, individual research project, or another officially recognised project format;
      • a list of courses you have already taken with me;
      • a short CV with a clear description of your interests and strengths concerning project or thesis work;
      • a transcript of grades or an overview of completed courses and credits for the courses you have taken so far;
      • a short description of your programming experience, especially in C/C++;
      • a short description of your previous experience with numerical simulation, scientific computing, physics, mathematics, or data analysis;
      • if you already have your own project idea: a short description of the goal and the scientific merit.

      For individual research projects, please also indicate the intended academic context of the project, the expected workload or credit points, whether the project is to be graded, and whether there are any formal requirements from your degree programme.

      For doctoral research projects, please also indicate your previous research experience, the intended scientific direction, and the doctoral framework in which the project would be embedded.

      If you want to do your thesis or project with a company or another department, please provide the following additional information in a concise format. Concise means that you should not write more than one paragraph for each of the items below and that your text should be concrete and understandable for a non-expert. The purpose of this information is to allow me to check whether the planned work has sufficient scientific merit with respect to the field of computer science.

      What is the expected or aimed-at outcome?

      • How does your approach and the expected result differ from the state of the art?
      • How do you plan to evaluate your work?

      Supervision must be provided by the company or the other department, and the supervisor should provide an evaluation report at the end of the thesis in a format to be discussed with me.

      Generic requests without sufficient information cannot be assessed. I can only assess whether supervision is realistic if I have enough information about your background, preparation, and scientific interests.

      The following information is provided in German because it concerns local procedures for scholarship recommendation letters, and evaluation reports at Frankfurt UAS.

      Beachten Sie Folgendes: Anfragen zur Erstellung eines Gutachtens für Bewerbungen um ein Stipendium oder für vergleichbare Bewerbungsverfahren müssen zusammen mit den entsprechenden Unterlagen (Lebenslauf, Zeugnisse, Leistungsnachweise und Ausschreibung bzw. Anforderungen der Förderinstitution) mindestens vier Wochen vor Ende der Bewerbungsfrist eintreffen.

      Voraussetzung für ein erfolgversprechendes Gutachten ist, dass der Bewerber aus mindestens einer Lehrveranstaltung, einem Seminar, einer Projektarbeit oder einer betreuten Abschlussarbeit persönlich und fachlich bekannt ist und dort mindestens die Note „Gut“ (1,7) erzielt hat. Andernfalls können keine belastbaren Gutachten verfasst werden.

      Bitte berücksichtigen Sie, dass ich allein aufgrund der Teilnahme an einer Vorlesung und der damit verbundenen Note im Regelfall kein Vorschlagsgutachten und keine substanzielle Empfehlung verfassen kann. Auf dieser Grundlage kann ich weder glaubwürdig darlegen, Sie hinreichend zu kennen, noch eine belastbare Beurteilung Ihrer fachlichen Selbständigkeit, Ihrer Diskussionsbeiträge, Ihrer Arbeitsweise und Ihrer Person abgeben. Für solche Gutachten sollte der Gutachter Sie aus einem Seminar, einer Projektarbeit, einer betreuten Abschlussarbeit oder einer vergleichbaren intensiveren akademischen Zusammenarbeit kennen.

      Grading scheme for a Thesis

      The final grade of the thesis is based on four main aspects:

      1. Quality of the conceptual/theoretical work. This includes aspects such as: 
        1. How well were the ideas thought out / the details worked out 
        2. How independent was the work 
        3. How meaningful and interesting/useful were the results
      2. Quality of the implementation work. This includes aspects such as: 
        1. Did you submit the full source code of your thesis
        2. How easily can we reproduce your work and your results 
        3. Is the code well documented, did you adhere consequently to a proper coding style
      3. Quality of the evaluation. This includes aspects such as: 
        1. Is the experimental/numerical setup well described 
        2. If Applicable: Is the selection of datasets reasonable (there should be at least two, with different characteristics) 
        3. Is there a comparison with a reasonable baseline or competitor method 
        4. Are the results correct 
        5. Are the results properly discussed 
      4. Quality of the write-up. This includes the aspects of:
        1. Style of writing: Interesting and enlightening or boring with many unnecessary repetitions
        2. Useful layout of the thesis as a whole: Titlepage, chapters, sections, TOC, TOF, etc.
        3. Usefule citations, correct bibliography with a unique citation style
        4. Number of pages
        5. Correct spelling, grammar, notation, and terminology.

      In this section, we discuss five golden rules: "simple sentences", "well-defined terms", "consistency", "be concrete", and "clear context". If you follow all five, your paper will be easy to read and understand. If these rules are ignored, the thesis will become unnecessarily difficult to read and may fail to communicate the scientific content clearly. The subsections below give a short description for each of these rules.

      Simple sentences

      A very simple but important rule of thumb is: write simple sentences. A simple sentence has the form: subject, predicate, object. If your sentence has two or more verbs, consider splitting it into two simpler sentences. It is rarely necessary to have sentences with more than two verbs. The sentences in this document are good examples for this writing style. 

      As a counterexample, this sentence, which is rather convoluted due to various reasons, including, for example, these nested relative clauses, puts a rather heavy cognitive load on the reader and is also simply too long by way of making several statements all in one sentence which could have just as well been made in, you can guess it, multiple sentences, one sentence per statement. 

      You get the idea! Note that using simple sentences may not be the best style when writing a novel. But it is perfect for scientific text, because it is easy to read and easy to understand. 

       

       

      Consistency

      If you use a particular word to describe something, stick to that word. Variation is maybe nice in a novel. But in scientific work, every single inconsistency makes it harder for the reader. It is also fine to repeat the same word again in the next sentence. In scientific work, this is even good style. 

      For example, it is perfectly OK to write: "In this section, we describe the details of our algorithm. The algorithm proceeds in two phases". You might be tempted to write the second sentence as "It proceeds in two phases". This would actually make the text harder to read because a reader has to resolve what "It" refers to (it could refer to "our algorithm", but also to "this section"). It would be even worse to write "The procedure proceeds in two phases". Now you have introduced a new word and the reader will be confused about whether "The procedure" is referring to "our algorithm" or maybe something else which they have overlooked. 

      Visual consistency is also important. If you use different fonts, use them with a consistent meaning. If you have multiple tables or figures in your thesis, make sure they have a consistent look.

      Be Concrete

      Be concrete, as opposed to vague. It is very easy to be vague with natural language. This is especially true if you have not fully understood something or if you don't know the details, but you want to make a statement nevertheless. 

      An example of a vague sentence is: "We show that our algorithm is much better than previous ones". First, the quality measure is not clear: better in exactly which way? Second, it is not clear how much better: 10% better, twice better, ten times better? Third, which previous algorithms: all of them them, some of them, and if only some of them, which ones? 

      Concreteness is particular important in the parts, when you define your problem or describe your solution or your experimental setup. For example, consider: "The goal of this paper is to get text from PDF documents". This sentence is unconcrete on so many levels. What does "get" mean, in which format should be text be "gotten", what constitutes a good solution, etc.

      Provide Clear context

      The first four rules are easy to understand and check. This fifth rule is a bit more subtle, but just as important as the previous four rules. Often a text is hard to read or understand and you can't really put your finger on why. That is usually because there are many sentences, where the context is unclear or missing. 

      So what does context refer to? When you explain something non-trivial, you need multiple sentences. Each sentence of your explanation will refer to entities or statements from earlier sentences. It is important that it is 100% clear what these references refer to. Otherwise the reader has to pause and think or even guess, both of which are a major nuisance when reading. 

      For example, consider the sentence "We bridge the gap between entities and text using automatic information extraction". This sentence is impossible to understand in isolation. It speaks of "the gap between entities and text". To understand it, one needs to understand: (1) what exactly "entities" refers to, (2) what exactly "text" refers to, and (3) what exactly is meant by the "gap" between these two. If the immediately preceding sentences make this clear, the sentence is fine. If they don't make it clear, the sentence remains cryptic. 

      Note that another issue with the example sentence might be the term "automatic information extraction". That term would be OK if it has been defined before or if it is defined right in the next sentence. If only "information extraction" was defined, the "automatic" would be an example of a vague term that is not concrete enough (Golden Rule #4).

      English or German?

      You can write your thesis in German or in English. If you write it in English, more people will read it. Plus, it's a great opportunity to practice writing in English. You will almost certainly need that ability in your later job, so why not start now. If you don't want anyone to read your thesis, write it in French or Latin.

       

      American English or British English?

      American English please. For example, write analyze (AE) and not analyse (BE). Write neighbor (AE) and not neighbour (BE). Write labeling (AE) and not labelling (BE). As a rule of thumb: if two variants of a word come to your mind, use the ones with less letters and the one with a z instead of an s

       

      How much should I write?

      A typical question is: how much should I write? My typical answer is: write as much as is necessary to understand what you did, how you were doing it, and all the aspects listed in the section Structure of a thesis below. No more, no less. The thesis should be self-contained. That is, for someone with a basic education in computer science, it should not be necessary to read anything else than your thesis in order to understand all the main aspects of your thesis. 

       

      How much related work should I look at?

      Whatever you do, there is probably lots of previous work at least on the general topic of your project or theses, and maybe even on the particular problem you are dealing with. Screening all this work (and first finding it, which can also be non-trivial) can take an arbitrary amount of time and easily several months. This gives rise to the question, how much time you should spend on it and when. 

      The short answer is that you should avoid the two extremes. One extreme is to ignore previous work until you are done with the thesis and then do a few Google searches to hack together a related work section. The other extreme is to spend many months on finding and screening related work before even starting to work on the problem yourself. 

      A good compromise is as follows. Before you start with your work, spend a day or so on searching the web (in particular, Google Scholar) on the general topic of your project or thesis. If you don't find anything on the particular problem you are dealing with, widen your search. There certainly is something about the general topic. Often, other people use other terminology to describe very similar or even the same things. So be creative when searching. 

      One good strategy is to look for well-cited papers or surveys. Such papers serve three purposes. First, the paper itself will tell you something interesting about the topic and the state of the art. Second, the paper will provide references to other work that might be relevant. Third -- and this is relevant especially if the paper is already a bit older -- you can look for newer papers citing this paper (this is easy in, say, Google Scholar). Note that all three only work well for high-quality papers, hence the remark about the citation count. A low-quality paper might be unaware of other related work and it might be ignored by the community and is hence not cited by other related work. (However, also great papers are sometimes ignored by future papers on a related topic because the authors of those papers did not do their homework.) 

      However, don't wait too long before starting your own work. But don't forget to look for related work either. While you work on the problem yourself, you should keep looking for related work as a background process. The more you understand about the problem yourself, the easier it will be to find other related work. Keep note of these works. In fact, it's best to write a paragraph or two about each paper you encounter right when you encounter it. That way, you will already have all the material you need when you have to write your Related Work section in the end. 

      For how to actually write the Related Work section, see the corresponding subsection the section  Structure of a thesis below. 

       

      "We" or "I" or passive voice?

      There is no clear answer or recommendation for that one. Look at peer-reviewed research papers and notice the style of writing. If you want to use "We", that is ok also if you are the only author. In many contexts, "we" can be interpreted as "the reader and I". "I" is very rarely used in scientific writing.

       

      Spelling, Hyphens, Commas

      As a final step, ALWAYS run a spell checker over your write-up. It is very embarrassing indeed if your write-up contains mistakes that any spell checker would have found. 

      Computer-science articles contain many multi-word noun phrases. A common question is when (not) to put a hyphen. There is actually a clear and simple rule for this: multi-word noun phrases have a hyphen only if you use them as an adjective. Here is an example: (1) This problem has a large scale. (2) This is a large-scale problem. Putting a hyphen in (1) would be a mistake. Not putting the hyphen in (2) would be a mistake. Understand the purpose of the hyphen. It says what belongs together. In sentence (2), without the hyphen, it would not be clear whether it is a "scale problem" that is large, or whether the problem has a "large scale". 

      Another common question is when (not) to put a comma. The rules in the English language are less strict than in the German language, but they are still pretty clear in most cases. For example, you should always put a comma after introductory clauses like "However", "In this section", "Therefore", ... You should also always include relative clauses into commas. As a rule of thumb: when you have two statements in one sentence and the separation of the two statements is not 100% clear, put a comma to clarify this. That is, in fact, the main purpose of a comma. 

      Finally, commas can save lives. For example: Let's eat, Grandma. 

       

      Structure of a thesis

      This section provides a list of the typical elements of a thesis, together with short descriptions. In facts, these are the typical elements of any scientific article or paper. 

      Every section and every subsection -- except the Abstract and the Introduction -- should start with a small introduction that tells the reader what comes next. The following four sentences are an example: "In this section, we will give a high-level description of the algorithm. The algorithm has two phases. In the first phase, ... (Section 3.1). In the second phase, ... (3.2)." Note how simple the sentences are (Golden Rule #1). 

      A good way to start your write-up is to write down all the section and subsection headers. The names of the headers should be carefully chosen, and they should be consistent in their style. 

       

      Title

      As a minimum, a title should be informative. Think of it as the shortest way to summarize your work in one short sentence. A secondary criterion is that it is catchy. This is not necessary, however, and sometimes hard to achieve. Also a catchy title should be informative. If you build a system, a typical title is the name of the system, followed by a colon, followed by a short description of what the system does. Avoid titles that cannot be understood before reading any of the contents. 

       

      Abstract

      An abstract should be self-contained and understandable to a non-expert. Think of it as the shortest way to summarize your work in one paragraph. As a minimum, it should clarify the problem dealt with in the thesis, and the main results that were obtained. If a short example can be given, it should be given, but this is not always possible. If space permits, add a sentence or two about the underlying techniques and how the thesis advances the state of the art. 

       

      Introduction

      An introduction should be self-contained and understandable to a non-expert. Think of it as the shortest way to summarize your work in a couple of pages. As a minimum, it should clarify the problem dealt with, the motivation for dealing with that problem, the main results, the main challenge and the line of attack used to overcome it, and how it advances the state of the art. In the abstract, you have only one or two sentences (if any) for each of these aspects. In the introduction, you have more space. For the problem statement, it is almost always a good idea to provide a figure or screenshot with a (carefully chosen) example. 

      One common mistake (and nuisance) is to have relative vague and informal statements in the introduction and then later a separate section with a more formal problem statement. The corresponding reader experience is that upon first reading one either does not understand the introduction or does not find it very useful or both. You can fix this as follows: whatever is relatively easy to understand and can already be defined in the introduction should already be defined in the introduction. 

       

      Related Work

      Also see the FAQ "How much related work should I look at?" above. 

      Most probably, other researchers have worked on the problem of your thesis, or a strongly related problem, before. Your thesis should include a section which summarizes the most relevant of these works. Typically this is Section 2, right after the Introduction. For each of these works, it should be explained in a nutshell what they do, what they achieve, and how this differs from what you do in your thesis. This should be understandable without the need to actually read the papers referred to. For each related work, think of the description as the shortest way to say this in one paragraph. 

      When there is a lot of work about a particular topic / problem, it is ok to focus on the most recent / most advanced approaches. Where there is no work on the exact problem from your thesis, the Related Work section should be about work on similar problems and it should explain how these related problems differ. Note that sometimes a problem looks different only on the surface, and the solutions can actually be applied to your problem as well.

       

      Future work

      Most probably, your work will leave various open ends. Make a list of what could be done next to improve on what you did. For each item, give a short description of the possible improvement + an idea for how, in principle, it could be achieved. Also give an estimate for the necessary time to realize that improvement (hours, days, weeks, months). Order your list by importance / significance. That is, the thing that should be improved next / gives the biggest improvement should come first. 

       

      Bibliography

      Make sure that the entries in your bibliography have a  consistent style. That is, abbreviate all conferences in the same way or use the full names for all, but do not mix the two styles. Same for author names. Same for capitalization of titles. Same for page numbers. It makes a very careless and untidy impression if the bibliography entries are inconsistent in their style. Note that it would be a mistake to put a comma before the "if" in the previous sentence.

      Teaching (Lehre)

      Teaching (English summary)
      I regularly teach core undergraduate courses in calculus, discrete mathematics, physics (with laboratory), and computational engineering / scientific computing. My teaching combines rigorous mathematical foundations with a strong emphasis on physical intuition, numerical methods, and hands-on computational and experimental work. The primary language of instruction in the bachelor’s programs is German.

      Regelmäßig gehaltene Lehrveranstaltungen (Auswahl)

      Analysis I (im Wintersemester)
      Grundlagen der höheren Mathematik für Ingenieur- und Naturwissenschaften.

      Diskrete Mathematik (im Sommersemester)
      Logik, Mengen, Relationen, Graphen, kombinatorische Methoden und Grundlagen der Zahlentheorie.

      Physik I
      Klassische Mechanik, Schwingungen und Wellen; begleitendes Labor mit grundlegenden Experimenten.

      Physik II
      Elektromagnetismus, Optik und moderne Physik; begleitendes Labor mit einfachen Experimenten-

      Computational Engineering Science (Wahlfach)
      Einführung in numerische Methoden und wissenschaftliches Rechnen für Ingenieure.

      Simulation und Optimierung (mit Labor)
      Modellierung, numerische Simulation und Optimierung technischer Systeme.

      Einführung in die Programmierung I (mit Übung/Labor)
      Grundlagen des Programmierens und algorithmischen Denkens für Studenten der Ingenieurwissenschaften.

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      last updated on: 06.13.2026