Student Projects
If you are interest in a project in the laboratory, feel free to contact any member even if no project is posted in SiROP
Designing the Next Generation of Injectable Drug Depots
Many drugs fail to achieve optimal therapeutic outcomes due to limitations of conventional delivery routes, such as poor oral bioavailability or the need for frequent intravenous administration. These challenges are particularly pronounced in chronic therapies that require stable, long-term drug exposure. Injectable depots, including subcutaneous systems, offer a promising alternative by forming local drug reservoirs that enable sustained and controlled drug release over extended periods. Polymer-nanoparticle (PNP) hydrogels represent an emerging class of injectable depot materials, combining injectability, viscoelasticity, and the ability to encapsulate and protect therapeutic agents. This project aims to investigate strategies to stabilize and industrialize drug-loaded PNP hydrogel formulations, with a particular focus on sterilization and dehydration approaches that preserve their structural and functional properties. The outcomes will contribute to the development of clinically and industrially viable injectable depot systems for long-acting therapies.
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Semester Project , Master Thesis
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Published since: 2026-01-06 , Earliest start: 2026-01-06 , Latest end: 2026-12-31
Organization Macromolecular Engineering Laboratory
Hosts Guzzi Elia
Topics Medical and Health Sciences , Engineering and Technology , Chemistry
Smuggling Drugs in Pumpkins: Integration of Host-Guest Assemblies into Hydrogels for Controlled Drug Delivery
Hydrogels are water-swollen polymer networks that have been increasingly explored as drug delivery systems. Their physicochemical properties can be precisely tailored, enabling spatial and temporal control over the release of the therapeutic molecules into the surrounding environment. One strategy to tune the release kinetics is by introducing drug–polymer interactions. Those can be of covalent or physical nature with varying binding affinities, typically slowing down the cargo diffusion and thus prolonging its release. In this work, drug–polymer interactions are integrated in hydrogels through the use of cucurbit[8]uril (CB[8]) host–guest complexation. We employ the pumpkin-shaped CB[8] to engineer a universal cross-linker that can encapsulate a variety of cargo molecules and release them in a controlled manner.
Keywords
Hydrogels, drug delivery, host-guest complex, biocompatibility, biomedicine, release studies
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Semester Project , Master Thesis
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Published since: 2026-01-05 , Earliest start: 2026-02-02 , Latest end: 2026-09-30
Organization Macromolecular Engineering Laboratory
Hosts Petelinsek Nika
Topics Engineering and Technology , Chemistry
Optical Glucose Sensor for a Perfusion System
Organ perfusion is a method by which blood and other fluids are oxygenated and pumped through organs including livers, kidneys, lungs and hearts in order to provide the organ with oxygen and nutrients. Various organ perfusion technologies are already in clinical use to improve organ preservation or even treat organs prior to transplantation. Furthermore, ex-vivo perfusion offers the unique opportunity to study whole organs as an isolated system. Robust organ perfusion systems require close control of perfusate parameters such as pH, oxygenation, flow, pressure, glucose concentration etc. While some parameters such as flow and pressure can be monitored with reusable sensors, others like pH, oxygenation and glucose require disposable sensors that are expensive and can only be used once. Because of high costs of these sensors, most perfusion systems are either too simplified and don’t account for these parameters or are so expensive that only very few laboratories conduct research with them. Therefore, development of cheap sensors, that can be easily produced, is important to advance research in organ perfusion.
Keywords
Optics, Glucose, Perfusion, Human
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-12-19 , Earliest start: 2026-02-01 , Latest end: 2026-11-01
Organization Macromolecular Engineering Laboratory
Hosts Binz Jonas
Topics Engineering and Technology
Temperature-Dependent Mechanic of Highly Entangled Polyacrylamide-Based Hydrogels
This project explores the temperature-dependent mechanical behavior of highly entangled poly(acrylamide-co-oligoethylene glycol acrylate) hydrogels, with the aim of linking experimental stress–strain data to predictive mathematical models for viscoelastic materials. These polymer networks are characterized by high monomer concentrations, low initiator content, and strong topological entanglements, making them an excellent experimental platform for testing modern theories of polymer dynamics. The student will systematically study how mechanical properties such as stiffness, hysteresis, stress relaxation, and nonlinearity evolve with temperature and deformation rate. Mechanical testing will be performed over a controlled temperature range to generate high-quality stress–strain data under compression and, where relevant, cyclic or rate-dependent loading. A central goal is to assess whether existing molecularly informed viscoelastic models can accurately capture the observed behavior, particularly in regimes where entanglements dominate the mechanical response. The project is carried out in close collaboration with a second research group specializing in theoretical and mathematical modeling of soft matter. This provides a rare opportunity to work at the interface between experiment and theory: experimental observations will directly inform model validation, refinement, and parameter selection. The broader motivation is to improve the predictive power of constitutive models for soft, polymer-based materials, which are widely used in applications ranging from adhesives to damping and load-bearing soft solids. This project is well suited for a student who is interested in polymer physics, rheology, or soft-matter mechanics, and who enjoys quantitative thinking. Independence, curiosity, and a willingness to engage with both experiments and theory are essential, as the project offers significant freedom to shape the experimental strategy and contribute original insights.
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Semester Project , Course Project , Internship , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)
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Published since: 2025-12-15 , Earliest start: 2026-02-16
Organization Macromolecular Engineering Laboratory
Hosts Mommer Stefan
Topics Engineering and Technology , Chemistry
Engineering of Gastroretentive Drug Delivery systems
Polymer-based drug delivery systems play a central role in shaping modern therapeutics, offering controlled release behavior and improved patient compliance. Yet traditional formulation development still relies largely on resource-intensive and time-consuming iterative experimentation to balance the complex interactions between polymer composition, processing parameters, and drug release behavior. This project aim to solve these limitations by developing a machine learning (ML) algorithm to accelerate the rational design of polymer formulations tailored to specific release profiles. By integrating active ingredient’s encapsulation studies, rheological and mechanical analyses, and detailed release-kinetic profiling, the work aims to build a comprehensive understanding of how formulation variables govern functional performance. The resulting dataset will later support data-driven modelling, enabling faster identification of promising compositions. This approach not only streamlines pharmaceutical development but also advances sustainable practices by minimizing material waste, positioning data-driven formulation as a cornerstone of next-generation smart therapeutics. This project focuses on the optimization and characterization of a novel gastroretentive drug delivery system (GARD) loaded with a selection of active compounds from a pre-established library of molecules. Comprehensive chemical and mechanical analyses will be conducted, encompassing encapsulation efficiency, rheological behavior, drug release kinetics, and the stability of encapsulated active pharmaceutical ingredients (APIs). A curated dataset of API-loaded polymer formulations and their associated properties will be later used to train predictive models, enabling the identification of optimal formulations with tailored release profiles.
Keywords
Drug Delivery Polymer formulation Advanced manufacturing Machine learning
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Semester Project , Internship , Master Thesis
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Published since: 2025-11-26 , Earliest start: 2025-12-01 , Latest end: 2026-12-31
Applications limited to Balgrist Campus , EPFL - Ecole Polytechnique Fédérale de Lausanne , ETH Zurich , Fernfachhochschule , Zurich University of Applied Sciences , University of Zurich , University of Basel , University of Berne , Institute for Research in Biomedicine , Hochschulmedizin Zürich , Empa
Organization Macromolecular Engineering Laboratory
Hosts Guzzi Elia
Topics Medical and Health Sciences , Engineering and Technology , Chemistry