Master End Projects

  • Merve Gunes
    A machine learning assessment of multi-resolution remote sensing data for Natura 2000 dune habitat classification
    (2020, Oct. 2, TU Delft)
  • Fokke Johannes Dijkstra
    Breakpoint detection through neural nets
    (2019 TU Delft)
  • Manuel Huber
    Development of a Deep Learning Surrogate Model to Simulate MetOp ASCAT Observations with Land Surface Parameters
    (2019, TU Delft)
  • Pim Klaassen
    “Using Neural Networks to Model Behavior in Vessel Trajectories”
    (2019, TU Delft)
  • Renske Taylor
    Development of a 3D Image Analysis Method to Measure Blast-Induced Fragmentation at the Leveäniemi Mine
    (2019, TU Delft)
  • Konstantinos Chatzopoulos Vouzoglanis
    Eutrophication prediction in the Dutch coastal waters using remote sensing data and machine learning
    (2019, TU Delft)
  • Konstantinos Vlachos
    Investigation of meso-scale Sentinel-3 product along-track correlations and the potential of inter-track SSHA estimation using machine learning
    (2019, TU Delft)
  • Tom Sassen
    The influence of drone flightpath on photogrammetric model quality
    (2019 TU Delft)
  • Marloes Arts
    BRCA2 mobility analysis using Deep Learning and the Moment Scaling Spectrum
    (July 2018, TU Delft / EMC)

Bachelor End Projects

  • Maud Diepeveen
    Quantitative analysis of diffusive transitions of DNA repair proteins using Markov Chain methods
    (Sept., 2020, TU Delft / EMC)
  • Tom van de Kamp
    Contraction Analysis Method Optimization for Cardiomyocytes and Vascular Smooth Muscle Cells (VSMCs)
    (Aug. 2020, TU Delft / EMC)
  • Rana Sannia Ul Haq
    Comparing different methods to analyse the diffusive be- haviour of DNA repair proteins by single-particle tracking
    (July, 2017, TU Delft / EMC)

Research Projects

  • Tijmen de Wolf
    Deep learning based segmentation of 53BP1 foci
    (July, 2020, Tu Delft / EMC)
  • Kirsten van Kooij
    Analysis of microCT 3D images
    (Sep., 2020, TU Delft / EMC)