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Abschlussarbeiten, Bachelor- und Masterarbeiten
Sie suchen gerade eine Diplomarbeit, ein Thema für eine Bachelor oder Master Thesis? Dann sind Sie hier richtig. In diesem Bereich sind Abschlussarbeiten aus allen Fakultäten zu finden.
Beachten Sie auch den entsprechenden Stichwortindex.
Wenn Sie selbst eine Diplomarbeit ausschreiben wollen, lesen Sie bitte vorher unbedingt das 'Best Practice Manual Stellenanzeigen'.
24.04.2024
Bachelor / Master thesis: Building a mechanistic model for a new chromatographic multi-column device
Interested in modeling in the biotech world?
The project:
Building models has become an essential part of process development in the biotech industry. Process understanding and automation are advanced with these, while practical work can often be reduced. In this project, a model for purifying a tagged ligand will be designed and used for process automation. We apply a multi-column chromatography system (OCTAVE Bio, Tosoh). Your work will aim to characterize the system components and implement A mechanistic model in CADET, a Python-based application.
Your profile:
* Student of Biotechnology or similar with modeling interests
* Student of automation technologies or similar with biotech interest
* Eager to learn new
* Motivated to solve problems
* Independent work style
Your tasks in short:
* Characterize OCTAVE Bio with tracer experiments (hands-on lab work)
* Build a model of the system in CADET (Python-based)
* Adapt the model with chromatographic columns
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Kontakt: f.eilts@tum.de
24.04.2024
HiWi/ Research student / Thesis: Developing protocols on a liquid handling system (pipetting robot) for lab-based assays
We seek a highly motivated student interested in lab automation and biotechnological assays.
The project:
Laboratory automation is a time-saving but challenging task. Our Tecan Evo gives us infinitive options to take over routine pipetting tasks. And by this, implement some degree of automation in our lab. ;)
The robot can work on different pipetting tasks and assays using scripted protocols. However, the processes have to be well-defined and designed. This will be your task to work on. Examples are assays like total protein assays or binding experiments. Biotech knowledge is an advantage but not a must – coming from other disciplines, you can gain knowledge in this field.
Your profile:
* Studies not relevant
* High interest in automation
* A biotech background is a plus
* Eager to learn new things
* Creative thinking
* Motivated to solve problems
* Independent work style
The task in short:
* Develop protocols for automatable assay
* Program pipetting schemes on the Tecan Evo150
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Kontakt: f.eilts@tum.de
17.04.2024
This is how your model could look like
Master Thesis: Modelling of next-generation MEMS microphones for hearing aids
We are looking for students who like to work in an interdisciplinary team and intend to realize their master's thesis within our group at Professorship of Microsensors and Actuators embedded in the EU project Listen2Future.
Topic
Advances in material science have enabled the use of piezoelectric materials in MEMS microphones. In contrast to commonly used capacitive MEMS microphones, piezoelectric microphones require less energy and are, therefore, useful for mobile applications such as hearing aids. Another advantage of the piezoelectric microphone is the linear relationship between electrical voltage and force. This simplifies force-feedback control of the sensor and thus opens the possibility of increasing the sensor performance even more. Crucial for this is the design of the microphone membrane. Therefore, highly accurate models must be set up to simulate the microphone behavior.
Within this Master Thesis, you will set up a shell model of the microphone membrane to obtain a complete 3D model. Currently, a 2D axisymmetric FEM model is available, which cannot cover the full 3D behavior. A shell model could simulate additional resonance modes and promising membrane designs for force-feedback control.
Simulation is done in COMSOL Multiphysics, the gold standard software to simulate the coupling of different en-ergy domains.
Work packages
• Topic familiarization and literature survey
• Familiarization with COMSOL Multiphysics
• Setting up a shell model of the piezoelectric microphone membrane
• Comparison with the existing 2D axisymmetric model
• Investigation of new membrane designs for force-feedback control
• Documentation and presentation of the results
Requirements
Background in one of the following fields:
• Electrical Engineering
• Mechanical Engineering
• Physics
• Materials Science
• Related fields of study
Timetable
The thesis can be started immediately.
Contact
Til Friebe, M.Sc.
Doctoral Candidate
Phone: +49 89 28923129
Email: til.friebe@tum.de
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Kontakt: til.friebe@tum.de
16.04.2024
Scientific machine learning through physics-informed neural networks
We are seeking a highly motivated student to develop a novel framework for Physics-Informed Neural Networks (PINNs) that overcomes current limitations.
Basics of Physics-Informed Neural Networks (PINNs):
PINNs are a powerful machine learning technique that combines the strengths of neural networks and physics. Here's a breakdown:
Neural Networks: These are algorithms inspired by the human brain, capable of learning complex patterns from data.
Physics: Scientific principles governing the behavior of matter and energy.
PINNs leverage the data-driven learning power of neural networks while incorporating physical laws through governing equations (often described by Partial Differential Equations - PDEs). This allows PINNs to:
Learn from data: Analyze existing observations or measurements of a physical system.
Enforce physical laws: Ensure the learned model adheres to established physical principles.
Handle complex systems: Model intricate physical phenomena that might be difficult to solve with traditional methods.
Project Focus:
This project builds upon the foundation of PINNs and aims to develop PINNs model that can model 2 Dynamic Systems:
Spring Mass Damper System
Inverted pendulum
Furthermore, the models should be:
Independent of initial conditions: Produces accurate results regardless of the system's starting state.
Partially independent of external forces: While the type of force needs to be known, the model should be able to infer the force equation from data.
Independent of natural frequency: Applicable to various systems with different inherent oscillation frequencies.
Generalizable: Analyze the effectiveness of incorporating advanced neural network architectures like Recurrent Neural Networks (RNNs) to increase generalizability.
This thesis will, therefore, focus on the combination of data-driven ML model and Physics behind the dynamic systems to gain the benefits of both worlds. It would be part of the project to evaluate if PINNs trained on simulated data can be extended to real systems. Furthermore, it would be part of the project to evaluate the impact of known physical model, unknown physical model, known inputs to the real physical system, unknown inputs to the real physical system etc. and their pros and cons.
Project Benefits:
Opportunity to work on cutting-edge research at the intersection of physics and machine learning.
Hands-on experience in developing and implementing advanced neural network models.
Develop strong technical skills in machine learning and scientific computing.
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Kontakt: tanmay.goyal@tum.de
10.04.2024
Master's Thesis: Green hydrogen economy: business models for electrolyzers
With green hydrogen, Germany will provide a sustainable and economic profitable energy source in the near future. Scaling-up electrolyzers, the technique behind green hydrogen production, encompasses a comprehensive research including end-of-life treatments and business models. The objective of this master's thesis is to develop green hydrogen business models and to conduct a system analysis.
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Kontakt: sarah.hasslacher@tum.de
09.04.2024
Master´s Thesis about fungal biomaterials
Optimizing the fabrication and material properties of mycelium composites at the Professorship of Fungal Biotechnology in Wood Science (Prof. Dr. J. Philipp Benz)
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Kontakt: marcello.nussbaumer@tum.de
05.04.2024
Master's Thesis: Decarbonizing steelmaking - Assessing the social impacts of green steel production
The conventional steel industry is a CO2-intensive industrial sector and is responsible for a significant amount of greenhouse gas emissions. An alternative decarbonisation pathway is the production of primary steel via direct reduction with hydrogen. The objective of this Master's thesis is to assess the social impacts of the green steel production.
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Kontakt: inka.hahn@tum.de
05.04.2024
Simulation Model for Material Flows from End-of-Life Vehicle Recycling for Metals
A literature review of existing recycling processes for mainly steel, aluminum, and copper based on automotive scrap will be carried out. Based on the researched processes, a model for end-of-life vehicle recycling is to be developed. Based on the model, various material outputs are to be calculated by simulating the model input with respect to material content of end-of-life vehicles.
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Kontakt: dominik.reichert@tum.de
23.03.2024
Licca liber
Masterarbeit: Der bayerische Lech – Erarbeitung von Grundlagen für die Flussrenaturierung
Der Bayerische Lech zwischen Füssen und Augsburg wurde ab 1910 reguliert und seit 1950 energiewirt-schaftlich ausgebaut. Damit wurde die als Biotop-verbundachse bedeutendste nordalpine Wildfluss-landschaft, ursprünglich vergleichbar mit dem Tagliamento, in eine Kette von 23 Stauseen umgebaut. Nach 70 Jahren sind die Stauräume mit Sedimenten aufgefüllt und die energiewirtschaftlichen Konzessionen laufen in einigen Jahren aus, sodass ein Flussrückbau möglich wäre. Hierfür werden entsprechende Leitbilder gesucht.
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Kontakt: Thomas Wagner, wagner@tum.de
18.03.2024
Masterthesis: Explainable Artificial Intelligence for Real-time Assistance in Minimally Invasive Surgery
Masterthesis at the Center of Clinical Robotics at the Klinikum rechts der Isar in cooperation with the Technical University of Darmstadt.
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Kontakt: lars.wagner@tum.de
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