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Using machine learning to predict species diversity for agri-environmental results-based schemes.

  • ETH Zurich
  • Chair of Agricultural Economics and Policy D-MTEC

Agri-environmental schemes are important to promote biodiversity-friendly farming practices. This thesis aims to propose a result-based scheme that uses machine learning to predict species diversity.

  • Agricultural, Veterinary and Environmental Sciences
  • Master Thesis

Contextual Intelligence Using Ultra Low-Power Sensors and ML

  • ETH Zurich
  • Center for Project-Based Learning D-ITET

The project aims to compare and evaluate different sensing technologies for contextual intelligence that can detect and classify the presence and movement of people at workplace- or room-level. The current implementation, using infrared sensors, shall be compared to novel ultrasound time-of-flight sensors that are still not commercially available. The project is interested in the achieved tradeoff between accuracy, power consumption, and system limitations.

  • Digital Systems, Electrical and Electronic Engineering, Environmental Technologies
  • Bachelor Thesis, Energy Harvesting (PBL), Firmware (PBL), Machine Learning (PBL), Master Thesis, Microcontroller (PBL), PCB Design (PBL), Semester Project

Fast Pose-Graph Generation for Structure-from-Motion

  • ETH Zurich
  • Computer Vision and Geometry Group

The goal is the develop an algorithm that builds a pose graph from an unordered set of images as fast as possible. This is achieved by, first, building a minimal spanning tree from the images exploiting predicted similarity scores. Then the spanning tree is populated with additional edges until the pose uncertainty falls below a threshold in each vertex. This procedure is very important for Structure-from-Motion algorithms where the first step is generated such pose graphs.

  • Computer Vision
  • Master Thesis, Semester Project

Hybrid Climbing Robot incorporating bio-inspired design

  • ETH Zurich
  • Environmental Robotics Laboratory

The thesis aims to develop a new bio-inspired robot with a thruster-based adhesion to climb uneven surfaces.

  • Intelligent Robotics, Robotics and Mechatronics
  • Master Thesis

Automatic Generator for Differentiable Minimal Solver

  • ETH Zurich
  • Computer Vision and Geometry Group

Minimal solvers are of extreme importance to robust geometric model estimation (e.g., relative camera pose). Automatic generators take a set of input constraints (usually polynomial) and automatically generate a solver that returns the sought model parameters given a set of input data points. None of the recent automatic generators account for the problem of creating differentiable minimal solvers - a problem that gets more and more attention with the success of deep learning-based methods.

  • Computer Vision
  • Master Thesis, Semester Project

Wearable Vibrotactile devices for motor learning and rehabilitation

  • Harvard
  • Harvard School of Engineering and Applied Science

Wearable haptic interfaces are promising for applications in robotics, athletics, rehabilitation, biofeedback-based medical devices and much more. This project will focus on the development of a low-profile wearable interface with on-board processing and actuation to help accelerate motor learning of complex tasks. Skills: Mechanical engineering, embedded electronics, signal processing, programming, wearables fabrication, interest in dance or rehabilitation applications

  • Biomechanical Engineering, Biosensor Technologies, Electrical Engineering, Engineering/Technology Instrumentation, Rehabilitation Engineering
  • Collaboration, Internship, Lab Practice, Master Thesis, Semester Project, Student Assistant / HiWi

Simulation of an octupole Paul trap

  • ETH Zurich
  • Photonics Laboratory

The goal of this semester project is to simulate a trap geometry that presents a non quadratic potential.

  • Electrical and Electronic Engineering, Optical Physics
  • Semester Project

Cell Imaging-Based Diagnostic Platform for Patients with Rheumatic Diseases

  • University of Zurich
  • Bjoern Menze

Background Precision medicine based on cell-based assays has gradually gained popularity and is now essential for the treatment of patients suffering from diseases with complex treatment regimens, such as rheumatoid arthritis. Classifying patients according to their synovial fibroblast (SF) functional signature could lead to targeted therapies with a much higher success rate than currently available disease-modifying drugs. To achieve this goal, we have successfully developed a series of assays that enable functional screening of synovial fibroblasts and form the basis of a drug discovery approach for more effective personalized treatment. Aim The aim of this work is to develop a model for automatically predicting the cellular stage from single-cell microscopy images, as such a model would facilitate the personalization of treatments for patients suffering from rheumatic diseases. Therefore, the functional stage of synovial fibroblasts (SF) - the cells of interest - should be classified into biologically meaningful classes based on physiological processes such as mitochondrial activity, oxidative stress or apoptosis. Since some cells cannot be clearly assigned to a specific class, it may be interesting to use not only supervised but also semi- or unsupervised approaches. All in all, the final goal is an easy-to-use pipeline for single cell segmentation and classification that provides biologically meaningful outputs and visualizations.

  • Artificial Intelligence and Signal and Image Processing
  • Master Thesis

Internship/ Master or bachelor Thesis: Machine Learning for Assessment of Walking Patterns in the SCI population - Time Series Classification

  • ETH Zurich
  • Sensory-Motor Systems Lab Other organizations: Spinal Cord Injury & Artificial Intelligence Lab

Gait patterns in multiple impairments present unique and complex patterns, which hinders the proper quantitative assessment of the walking ability for chronic ambulatory conditions when translated to daily living. In this project, we will focus on finding clusters of gait patterns through unsupervised learning from a large dataset of incomplete spinal cord injury individuals. The goal is to investigate hidden patterns in relation to the type of injuries and find their application for future diagnosis and rehabilitation treatment. Your work will guide future rehabilitation methods in general clinical practice, through applied classification and dimensionality reduction in Biomechanics of walking. Goal: Develop an unsupervised clustering pipeline for a large dataset of gait patterns from spinal cord injured individuals for class similarity evaluation

  • Engineering and Technology, Expert Systems, Medical and Health Sciences, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Pattern Recognition, Signal Processing, Simulation and Modelling
  • Bachelor Thesis, ETH Zurich (ETHZ), Internship, Master Thesis

Betriebliche Optimierungsmodelle in der Schweizer Landwirtschaft

  • ETH Zurich
  • Chair of Agricultural Economics and Policy D-USYS

Basierend auf Ihren Kenntnissen aus der Vorlesung «Optimierung landwirtschaftlicher Produktionssysteme» erstellen Sie ein Optimierungsmodell in Excel oder R und beantworten damit eine von Ihnen erarbeitete Forschungsfrage.

  • Agricultural Economics, Environmental Sciences, Operations Research
  • Bachelor Thesis
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