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Max Planck ETH Center for Learning Systems

AcronymMPG ETH CLS
Homepagehttp://learning-systems.org/
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TypeAlliance
Current organizationMax Planck ETH Center for Learning Systems
Members
  • Autonomous Motion Department
  • Agile & Dexterous Robotics Lab
  • Group D'Andrea
  • Automatic Control Laboratory
  • Multiscale Robotics Lab
  • Autonomous Systems Lab
  • Applied Mechanobiology - Prof. Viola Vogel
  • Sensory-Motor Systems Lab
  • Vision for Robotics Lab
  • Robotic Systems Lab
  • Photogrammetry and Remote Sensing (Prof. Schindler)
  • Machine Learning and Computational Biology Lab
  • Information Science and Engineering
  • Seminar for Statistics
  • Data Analytics
  • Learning and Adaptive Systems
  • Computational Biology Group
  • Algorithms, Data Structures, and Applications
  • Group Zeilinger
  • Computer Vision and Geometry Group
  • Computer Vision Laboratory
  • Rehabilitation Engineering Lab
  • Institute of Neuroinformatics
  • Advanced Interactive Technologies
  • Interactive Geometry Lab
  • Translational Neuromodeling (TNU)
  • Institute of Neuroinformatics
  • Empirical Inference Department
  • Probabilistic Numerics Group
  • Perceiving Systems Department
  • Micro, Nano, and Molecular Systems (Peer Fischer)
  • Physical Intelligence (Metin Sitti)
  • Biomedical Informatics Group


Open Opportunities

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Electric Vehicle Speed Tracking Control Algorithm

  • ETH Zurich
  • Automatic Control Laboratory

This master project is about designing a control algorithm for speed tracking in electric vehicles while considering the reliability of the system.

  • Automotive Engineering, Mechanical and Industrial Engineering, Systems Theory and Control
  • Master Thesis, Semester Project

Data-driven control of paralleled grid-forming and grid-following inverters

  • ETH Zurich
  • Automatic Control Laboratory

This project suggests a supervisory control design for grid-forming (GFM) inverters, when operated in parallel with multiple grid-following (GFL) inverters in a microgrid (see Fig.1). The supervisory controller only acts on the P and Q setpoints of the GFM inverter, such that the stability of the overall network is maintained during transient responses.

  • Electrical Engineering, Systems Theory and Control
  • Master Thesis

Dynamical analysis of the Walenstadt grid

  • ETH Zurich
  • Automatic Control Laboratory

The increasing number of renewable energy sources, such as Photo Voltaic (PV) systems, has made the shift from centralized to distributed generation in power systems. However, growing share of renewable sources in the energy demand poses a challenge to power system stability due to lack of inertia. System inertia refers to the kinetic energy stored in large generators’ rotating mass, such as those found in fossil-fuel based power plants. System inertia is vital for maintaining a stable frequency level. This work is part of an NCCR project with the utility company of Walenstadt, Switzerland, to explore the transient response studies of the network under different scenarios, such as change in the load or fault conditions.

  • Electrical Engineering
  • Master Thesis

Reliable control of a DC link in a power converter

  • ETH Zurich
  • Automatic Control Laboratory

Capacitors are essentials components of most power electronic converters, which suppress voltage fluctuations and absorb ripples. Nevertheless, capacitors suffer from thermal and electrical issues, and their weaknesses are a limited life cycle and high degradation catastrophe rate. Research has found that capacitors cause about 30% of the faults in converters due to aging degradation, making them the most vulnerable part of electronic power converters. With the rising penetration of converters for transmission in networks, the stability of converters to certify system operation safety is crucial.

  • Electrical Engineering, Systems Theory and Control
  • Bachelor Thesis, Master Thesis, Semester Project

Uncertainties in Continual Learning

  • ETH Zurich
  • Computer Vision and Geometry Group

In continual learning, deep learning models incrementally learn more classes or tasks over time. Doing so, they should not forget previously learned knowledge. This is a hard and active research problem. Making it even harder, we want the models to also estimate correct uncertainty. E.g., they should be highly uncertain about a new object type, but not uncertain about an object that they just learned correctly. [1] Parisi et al., Lifelong learning with neural networks http://dx.doi.org/10.1016/j.neunet.2019.01.012 [2] Gawlikowski et al., Uncertainty in Deep Neural Networks http://arxiv.org/abs/2107.03342

  • Engineering and Technology, Information, Computing and Communication Sciences
  • Master Thesis, Semester Project

Outlier-Robust Rotation Optimization using Harmonic Hierarchies of Polynomials

  • ETH Zurich
  • Automatic Control Laboratory

Rotation optimization occurs in camera calibration, kinematics, animation, and attitude setting of spacecraft. These optimization problems are generically nonconvex and difficult to solve, but some problem variations offer exact solutions in the low-noise and outlier-free setting. This project will investigate Harmonic Hierarchies of polynomials as a mechanism to perform global optimization of outlier-robust rotation alignment tasks. Harmonic Hierarchies utilize the properties of the rotation space and binary hypercube to produce a sequence of linear programs in increasing size, yielding a sequence of convergent upper-bounds and lower-bounds to the true alignment error. Extensions to this project can include multi-camera alignment (group averaging) and problems in inverse kinematics. This project will be performed in collaboration with Lucas Slot (ETH Zurich: D-MATH) and Mauricio Velasco (Universidad Catolica del Uruguay).

  • Engineering and Technology, Mathematics
  • ETH Zurich (ETHZ), Master Thesis, Semester Project

Design of voltage control incentives (with Swissgrid)

  • ETH Zurich
  • Automatic Control Laboratory

In this project, co-supervised by Swissgrid, we will look into the problem of designing incentives for the procurement of voltage support services in the grid. The design of an efficient mechanism requires the use of tools from control theory, game theory, and optimization.

  • Electrical Engineering
  • Collaboration, Energy (IfA), Master Thesis

Meta-Learning Control of a Ball-on-a-Plate System

  • ETH Zurich
  • Automatic Control Laboratory

The advertised project focuses on the implementation of meta-learning based control approaches on a ball-on-a-plate system, which requires redesigning the system’s electronics. Hence, this project combines electronics, control theory, image recognition and machine learning.

  • Artificial Intelligence and Signal and Image Processing, Electrical and Electronic Engineering
  • ETH Zurich (ETHZ), Master Thesis, Semester Project

Development of a Synchronization Pipeline for Multi-Modal, Multi-Source Timeseries Data: Collaboration with Tohoku University, Japan. + Potential option for a research stay in Japan.

  • ETH Zurich
  • Sensory-Motor Systems Lab

Modern robots collect data from various sensors. When these sensors operate independently, time-synchronization through rectification of their individual clocks and correction for temporal drift is required. In our previous work, we developed an initial version of a synchronization pipeline in Python, designed for offline data synchronization. Our current pipeline already effectively synchronizes sensors that include a common external synchronization signal. Despite already working well, our current pipeline still requires some expertise to configure the data sources. To make the pipeline widely usable, we now need to make it function seamlessly even without expert knowledge and access to external synchronization signals. This enhancement should also extend to scenarios involving continuous online data as well. Furthermore, we want to prove the correctness of the synchronization and showcase the performance based on synthetic data. In essence, your thesis will comprise the following key objectives: 1. Understand the challenges involved in data synchronization. 2. Familiarize yourself with the existing synchronization pipeline. 3. Innovate strategies for achieving data synchronization without relying on external synchronization signals. 4. Enhance the user interface by creating an intuitive guide for using the pipeline effectively. 5. Extend the functionality to accommodate online data streams. 6. Assess the pipeline's correctness and performance using synthetic biosignals, as well as pre-recorded biosignals from the SMS-Lab and Tohoku University. Throughout this project, you will receive guidance from me, a 4th year PhD candidate at the Sensory-Motor Systems Lab at ETH Zurich, and researchers at Tohoku University in Sendai, Japan. As I will be in Japan from October, we will conduct the weekly meetings over Zoom. Furthermore, in case of interest, you have the exciting opportunity to visit us in Japan. This opportunity can be pursued either through personal funding or by applying for respective scholarships, such as the Heyning-Roelli Foundation, SEMP, Spickenreuther Foundation, and others. I have received scholarships in the past and I am happy to provide guidance and support throughout the application process.

  • Biosensor Technologies, Data Storage Representations, Data Structures, Digital Systems, Information Storage, Retrieval and Management, Pattern Recognition, Signal Processing, Simulation and Modelling, Software Engineering
  • ETH Zurich (ETHZ), Internship, Master Thesis, Semester Project

Master Thesis / Project - SENSEI: Sensor Teaching in Multi-Activity classification from Video and Wearables for Wheelchair Users

  • ETH Zurich
  • Sensory-Motor Systems Lab Other organizations: ETH Competence Center - Competence Center for Rehabilitation Engineering and Science (RESC), Spinal Cord Injury & Artificial Intelligence Lab

In this project, we focus on continuous and quantitative monitoring of activities of daily living (ADL) in SCI individuals with the goal of identifying cardiovascular events and PI-related risk behaviors. ADLs specific to SCI patients and their lifestyles shall be discussed and narrowed down in the scope of this work, therefore an autonomous camera-based system is proposed to classify ADLs. The Current work builds on a previous project where a SlowFast network [1] was trained to identify SCI-specific classes and we aim to further improve the classification and temporal resolution for transferring to wearables' time-series data.

  • Computer Vision, Health Information Systems (incl. Surveillance), Intelligent Robotics, Knowledge Representation and Machine Learning, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Pattern Recognition
  • Bachelor Thesis, ETH Zurich (ETHZ), Internship, Master Thesis, MD
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