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Computational Modeling of Artificial Muscle Cells for Biohybrid Robots
This research aims to advance biohybrid robotics by integrating living biological components with artificial materials. The focus is on developing computational models for artificial muscle cells, a critical element in creating biohybrid robots. Challenges include modeling the complex and nonlinear nature of biological muscles, considering factors like elasticity and muscle fatigue, as well as accounting for fluid-structure interaction in the artificial muscle's environment. The research combines first principle soft body simulation methods and machine learning to improve understanding and control of biohybrid systems.
Keywords: Biohybrid Robotics, Computational Models, Soft Body Simulation, Machine Learning, Surrogate Models, Finite Element Method (FEM)
Are you a highly motivated and academically outstanding Bachelor's or Master's student? Are you passionate about exploring the intersection of physics, computer science, robotics, and biology? We are looking for students to join the Soft Robotics Lab at ETH Zurich for a Semester, Bachelor’s or Master’s Project and contribute to groundbreaking research in the field of biohybrid robotics. The objective in biohybrid robotics is to combine living biological components with artificial materials to create adaptable and functional systems capable of performing various tasks. As a part of our research group, you will be working on the development of computational models for muscle cells, a crucial element in the creation of biohybrid robots. Modeling the intricate structure and behavior of biological muscles poses a challenge due to their complex and nonlinear characteristics. Simulation models must capture the biomechanical and physiological aspects of muscle function, including factors like elasticity or muscle fatigue. Furthermore, artificial muscles exist within a fluidic environment, introducing the relevance of fluid-structure interaction (FSI). This also highlights the importance of the dynamic interplay between the deformable structures of the muscles and the surrounding fluid, further complicating the modeling and control aspects in biohybrid robotic systems. Our research involves leveraging both first principle soft body simulation methods and machine learning techniques to enhance our understanding and control of biohybrid systems.
Are you a highly motivated and academically outstanding Bachelor's or Master's student? Are you passionate about exploring the intersection of physics, computer science, robotics, and biology? We are looking for students to join the Soft Robotics Lab at ETH Zurich for a Semester, Bachelor’s or Master’s Project and contribute to groundbreaking research in the field of biohybrid robotics. The objective in biohybrid robotics is to combine living biological components with artificial materials to create adaptable and functional systems capable of performing various tasks. As a part of our research group, you will be working on the development of computational models for muscle cells, a crucial element in the creation of biohybrid robots. Modeling the intricate structure and behavior of biological muscles poses a challenge due to their complex and nonlinear characteristics. Simulation models must capture the biomechanical and physiological aspects of muscle function, including factors like elasticity or muscle fatigue. Furthermore, artificial muscles exist within a fluidic environment, introducing the relevance of fluid-structure interaction (FSI). This also highlights the importance of the dynamic interplay between the deformable structures of the muscles and the surrounding fluid, further complicating the modeling and control aspects in biohybrid robotic systems. Our research involves leveraging both first principle soft body simulation methods and machine learning techniques to enhance our understanding and control of biohybrid systems.
Possible work packages could be:
1. **Soft Body Simulation Methods:** Engage in the development and refinement of first principle models for simulating the behavior of muscle cells within the context of biohybrid robots. Conduct literature review of existing work in the field of muscle modeling.
2. **Machine Learning Surrogate Models:** Contribute to the integration of machine learning approaches for creating accurate surrogate models, enabling efficient and real-time control of biohybrid systems.
Possible work packages could be:
1. **Soft Body Simulation Methods:** Engage in the development and refinement of first principle models for simulating the behavior of muscle cells within the context of biohybrid robots. Conduct literature review of existing work in the field of muscle modeling. 2. **Machine Learning Surrogate Models:** Contribute to the integration of machine learning approaches for creating accurate surrogate models, enabling efficient and real-time control of biohybrid systems.
- Strong academic background with exceptional grades in physics, computer science, mechanical engineering, biomedical engineering, or related fields.
- Strong Python programming skills, ideally also C++.
- Proficiency in FEM-based simulations.
- Prior knowledge in muscle biology and machine learning is a plus.
- Enthusiasm for interdisciplinary research and a keen interest in soft robotics.
- Capable of both working independently and cooperating with mentors and teammates.
- Strong academic background with exceptional grades in physics, computer science, mechanical engineering, biomedical engineering, or related fields. - Strong Python programming skills, ideally also C++. - Proficiency in FEM-based simulations. - Prior knowledge in muscle biology and machine learning is a plus. - Enthusiasm for interdisciplinary research and a keen interest in soft robotics. - Capable of both working independently and cooperating with mentors and teammates.
Manuel Mekkattu, manuel.mekkattu@srl.ethz.ch, Soft Robotics Lab, D-MAVT, ETH Zürich
Prof. Robert Katzschmann, rkk@ethz.ch, Soft Robotics Lab, D-MAVT, ETH Zürich
If you are interested, please submit **a motivation letter, your CV, transcripts, and two references**.
Manuel Mekkattu, manuel.mekkattu@srl.ethz.ch, Soft Robotics Lab, D-MAVT, ETH Zürich
Prof. Robert Katzschmann, rkk@ethz.ch, Soft Robotics Lab, D-MAVT, ETH Zürich
If you are interested, please submit **a motivation letter, your CV, transcripts, and two references**.