Within a project, students will learn how to tackle a research project. They will work together with Phd-students and postdoctoral researchers and develop detailed knowledge of a topic of current research.
Here, initial MATLAB or Python simulations are supposed to accompany the project work. Furthermore, students will learn how to read scientific texts effectively and to report research findings in writing and in seminar presentations.
In a Bachelor/Master thesis, students will get insight into a topic of current research. They will learn how to use MATLAB or Python for implementing algorithms and carrying out simulations. Real data may be available as well, depending on the chosen topic.
The following list provides a selection of student projects which are currently offered by the Bioinspired Communication Systems Lab:
Current available projects:
Funded project in cooperation with BRAIN AG
Synthetic Biology
Modeling
- Bayesian optimization for the characterization of synthetic circuits
- Learning Sequential Chemical Reactions
- Relaxed continuous time Markov chains
- Entropic matching to approximate filtering distributions of chemical reaction networks
- Effects of asymmetry in gene expression noise on functionality of small gene networks
- Knowledge graphs for parameter optimisation in large-scale mechanistic models
- Deep Learning of Next-Generation-Sequencing Biological Data
- Investigation and Implementation of Deep Neural Network models in Whole Slide Images
- Machine Learning for Image Analysis and Cell Segmentation in Biological Research
- Learning non-Markovian multi-component temporal processes from biological high-throughput data
- Channel Capacity of Amplitude- vs. Frequency modulated regulation
- Spectral Methods for Markov Chain Aggregation
- Higher-order integration schemes for jump-diffusion models of biomolecular reaction networks
- Automated Design of Genetic (Logic) Circuits
Experimental Investigation
- Implementation of post-transcriptional regulated cell-free circuits into microfluidic chemostat
Collective Intelligence
Modeling
- Chiral active matter with nematic order
- Self-replication of colloidal particles
- Power grids and oscillatory dynamics
- Deep Reinforcement Learning for Multi-Agent Systems
- Learning in Mean Field Games
- Inverse Reinforcement Learning for Recovering Learning Properties
- Combining Rate Distortion Theory and Bayesian Modelling
- Pattern formation in chiral active matter
Experimental Investigation
- Reinforcement Learning for Drone Swarms
- Drone Swarms and Dynamic Structure Formation
- Decentralized Control of Drone Swarms
