Recent Submissions 

  1. Probing Gravity - Fundamental Aspects of Metric Theories and their Implications for Tests of General Relativity 

    Zosso, Jann (2024)
    Guided by the Einstein equivalence principle that identifies the phenomenon of gravitation as a manifestation of the dynamics of spacetime in contrast to a localizable force, we review and explore its consequences on formulating a theory of gravity. The resulting space of metric theories of gravity may address open conceptual and observational puzzles through a wealth of effects beyond general relativity, whose traces can be searched ...
    Doctoral Thesis
  2. Training and Certification of Neural Networks with Guarantees 

    Müller, Mark Niklas (2024)
    Provable safety and trustworthiness guarantees are crucial for the safe adoption of neural network based systems in mission-critical applications. However, despite significant efforts, both training networks that satisfy such safety and trustworthiness properties and verifying that they do so remain open challenges. In this thesis, we tackle both of these challenges and demonstrate that they are closely intertwined. We present two novel ...
    Doctoral Thesis
  3. Chemical and mRNA-based Cellular Reprogramming to Muscle Progenitor Cells for the Treatment of Muscular Dystrophy 

    Qabrati, Xhem (2024)
    Muscular dystrophies encompass a group of genetic diseases with a wide spectrum of clinical severities. Duchenne muscular dystrophy (DMD) is the most common and debilitating form of dystrophy, causing progressive muscle wasting and premature death due to cardiorespiratory failure. DMD is triggered by mutations in the DMD gene that is needed to stabilize myofibers. Its absence causes perpetuating cycles of myofiber necrosis and muscle ...
    Doctoral Thesis
  4. Novel approaches for synthetic live biotherapeutic product production using immobilisation 

    Verpaalen, Mathieu (2024)
    The human gut microbiome plays an important role in health and disease and can easily be manipulated. This makes the gut microbiome a promising target for therapeutic modulation. Groups of bacteria, also called consortia, are a means to specifically enrich the gut microbiome composition with potential missing beneficial functions. However, efficient production of these synthetic consortia is challenging. The current production strategy ...
    Doctoral Thesis
  5. Navigating collaboration for innovation both within and across organizational boundaries: An interaction-centric perspective 

    Varesco Kager, Nora (2024)
    Innovation is crucial for firms’ long-term success, and collaboration within and across organizational boundaries is crucial for innovation, making it important to understand collaboration demands and their navigation. To do so, scholars suggested taking a holistic approach by attending to interactions between relevant elements. Yet, while collaboration elements like tensions, challenges, and success-factors have been described, there ...
    Doctoral Thesis
  6. Quantification of Cellular Forces 

    Zhang, Xinyu (2024)
    Doctoral Thesis
  7. Agile and Efficient Inference of Quantized Neural Networks 

    Rutishauser, Georg (2024)
    The rapid proliferation of the internet of things (IoT) has coincided with a revolution in machine learning, driven by deep learning-based algorithms. At the intersection of these developments, smart sensing systems aim to integrate sensing, deep learning-based interpretation of the captured data and reaction to the results of the processing. As battery-powered IoT sensor nodes operate under stringent power, memory and compute resource ...
    Doctoral Thesis
  8. Lagrangian Cobordisms in Liouville Manifolds 

    Bosshard, Valentin (2024)
    The following thesis is based on the two articles and divided into two parts accordingly. The first part defines Lagrangian cobordisms in Liouville manifolds between non-compact Lagrangian submanifolds. Biran and Cornea showed that Lagrangian cobordisms between compact Lagrangian submanifolds can be used to study the triangulated structure of the derived Fukaya category. This statement is transferred to the non-compact setting to study ...
    Doctoral Thesis
  9. Measuring and modeling time use patterns in Switzerland 

    Winkler, Caroline (2024)
    As part of this dissertation, a novel survey methodology was developed for the collection of rich, longitudinal travel, time use, and expenditure data, leveraging the opportunities offered by both passive GPS tracking technology and time use diaries. Based on these data, patterns and preferences related to paid work, leisure, and other activities that comprise our everyday life were evaluated. Activities performed inside and outside of ...
    Doctoral Thesis
  10. A Software Framework for Bayesian Uncertainty Quantification, Optimization, and Reinforcement Learning 

    Wälchli, Daniel Thomas (2024)
    In this doctoral thesis, we present a high-performance computing framework tailored for stochastic optimization, Bayesian uncertainty quantification, and (inverse) reinforcement learning. The development of this framework has facilitated numerous applications across various domains: optimization and Bayesian uncertainty studies in biology and epidemiology, reinforcement learning techniques for drag reduction in a channel-flow, and inferring ...
    Doctoral Thesis
  11. New graph learning approaches for exploring gene and protein function 

    Muzio, Giulia (2024)
    Graphs serve as a universal tool for modeling intricate systems of interacting components, making them especially well-suited for representing knowledge on biological processes, including disease pathways, and complex biological molecules like proteins. In addition to providing a mathematical framework for representing biological components and their interactions, the use of graphs unlocks a suite of learning methods to extract insights ...
    Doctoral Thesis
  12. Ultrasound-driven Ciliated Microrobotic Systems for Biomedical Applications 

    Dillinger, Cornelius Benjamin (2024)
    Doctoral Thesis
  13. Atmospheric Modeling for Enhancing Air Quality Prediction through the Integration of Inverse Modeling and Data-Driven Approaches 

    Feng, Xiaoxiao (2024)
    Air pollution holds significant importance due to its potential to jeopardize human health, negatively impact ecosystems, and result in economic losses. Understanding the internal mechanisms of air pollution is crucial for representing and forecasting air pollution, thereby enabling the mitigation of its adverse effects. Air quality models are powerful tools that predict air pollution by learning the innate physical and chemical processes ...
    Doctoral Thesis
  14. Towards a Deterministic Polynomial Time Algorithm for the Exact Matching Problem 

    El Maalouly, Nicolas (2024)
    This thesis deals with graph problems that are solvable in randomized polynomial time but no deterministic algorithm exists for them. The central problem we focus on is the Exact Matching problem. In the Exact Matching problem, we are given a graph whose edges are colored red or blue and the task is to decide for a given integer k, if there is a perfect matching with exactly k red edges. Since 1987 it is known that the Exact Matching ...
    Doctoral Thesis

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