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Bacteria meet materials! From biomineralization to carbonate-based living materials

  • ETH Zurich
  • Complex Materials

Natural calcium carbonate is produced through a complex process determined by chemical, biological, physical, and anthropological factors whereas synthetic calcium carbonate is obtained by easy chemical protocols. Although the synthetic approach seems attractive due to the short synthesis time and control over the mineral microstructure, the reactants and products of this reaction can be toxic and thus being an unsustainable process. On the other hand, a bioinspired method based on mineralization induced by soil bacteria emerges as a sustainable alternative to synthesize calcium carbonate in a controlled manner. Biomineralization is a natural process that harnesses the biological and biochemical mechanisms of microorganisms to induce the precipitation of minerals intra or extracellularly. The polymorphs of bacterial-induced calcium carbonate are dictated by the chemical composition of the medium used for the culture of mineralizing bacteria as previously described. Despite biomineralization is already being exploited in the development of applications such as self-healing concrete, bio bricks, bio cement, among others, it remains still challenging to predict the resulting polymorph and control over the structural properties of the calcium carbonate based on the biological feature of the system.

  • Biochemistry and Cell Biology, Biotechnology, Chemistry, Engineering and Technology, Environmental Sciences, Medical and Health Sciences, Microbiology, Soil and Water Sciences
  • Bachelor Thesis, ETH Zurich (ETHZ), Master Thesis, Semester Project

Master thesis: The gut microbiome and endocrine disruptive chemicals.

  • ETH Zurich
  • Laboratory of Food & Nutrition Toxicology

Endocrine disruptive chemicals (EDCs) are chemicals that disrupt the human endocrine system by mimicking hormones or affecting their circulating concentrations in blood. Xenobiotic metabolism is the major mechanism for detoxification but can also lead to unwanted toxic metabolites. Recently, the gut microbiome has emerged as an important site of chemical biotransformation1 that can activate xenoestrogens. Our research in quantitative toxicology thus focuses on the role of gut microbiota in generating metabolites that potentially act as EDCs and the impact of gut metabolism on quantitative toxicokinetics. For this, we apply a mixed set of experimental and computational tools such as anaerobic fermentations, LC-MS/MS, kinetic modeling and in vitro toxicology assays.

  • Microbiology, Modeling and Simulation, Toxicology (incl. Clinical Toxicology)
  • Master Thesis

Mineralization Biosensors

  • ETH Zurich
  • Complex Materials

Natural calcium carbonate is produced through a complex process determined by chemical, biological, physical, and anthropological factors whereas synthetic calcium carbonate is obtained by easy chemical protocols. Although the synthetic approach seems attractive due to the short synthesis time and control over the mineral microstructure, the reactants and products of this reaction can be toxic and thus being an unsustainable process. On the other hand, a bioinspired method based on mineralization induced by soil bacteria emerges as a sustainable alternative to synthesize calcium carbonate in a controlled manner. Biomineralization is a natural process that harnesses the biological and biochemical mechanisms of microorganisms to induce the precipitation of minerals intra or extracellularly. The polymorphs of bacterial-induced calcium carbonate are dictated by the chemical composition of the medium used for the culture of mineralizing bacteria as previously described. Despite biomineralization is already being exploited in the development of applications such as self-healing concrete, bio-bricks, bio cement, among others, it remains still challenging to predict the resulting polymorph and control over the structural properties of the calcium carbonate based on the biological feature of the system.

  • Biology, Engineering and Technology
  • Internship, Master Thesis, Semester Project

Reviewing future cost of green hydrogen production.

  • ETH Zurich
  • Chair of Energy Systems Analysis

Low-carbon hydrogen is supposed to play a key role in decarbonizing global economies. Low-carbon hydrogen can be generated using different energy sources, for example, using renewables; also known as ‘green’ hydrogen. Currently, there is a wide variety of literature that evaluates the cost of green hydrogen production, however, they all have their underlying methods and assumptions that have a huge influence on cost estimations.

  • Environmental Engineering, Environmental Impact Assessment
  • Bachelor Thesis, Course Project, Internship, Master Thesis

Deep Learning for RNA Therapeutics

  • Harvard
  • Division of Gastroenterology, Hepatology and Endoscopy

Building on the success of mRNA COVID vaccines, RNA lipid nanoparticles (RNA-LNPs) have emerged as a new class of drug formulations. The student will work on developing deep learning models that, when integrated with high-throughput science, can design advanced RNA-LNPs with targeted properties, including specific organ targeting and extended stability. These formulations will be crafted for treatments in areas like cancer and regenerative medicine. While large language models (LLMs) like ChatGPT are predominantly used for text-related tasks, their potential in drug discovery remains largely untapped. The student will also explore the capability of LLMs to make AI models in drug discovery more intuitive and accessible.

  • Artificial Intelligence and Signal and Image Processing, Computational Biology and Bioinformatics
  • Master Thesis

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

Bioprinting living factory with microbes and microgels

  • Empa
  • Biointerfaces

Microbes such as bacteria and fungi produce a broad range of materials including polymers, biominerals, nanoparticles, antibiotics, and therapeutic proteins and peptides. Recent advances in synthetic symbiotic consortia, where more than one microbe are co-cultured, further broadened the range of microbial materials. This project aims to generate 3D functional materials by taking advantage of bioprinting and various microbes. In particular, the goal is to produce materials with ultra-high mechanical strength and toughness. Students with background knowledge in material engineering, chemistry and/or (micro)biology are highly encouraged to join this exciting project!

  • Biology, Chemistry, Engineering and Technology, Medical and Health Sciences
  • Bachelor Thesis, Internship, Master Thesis, Semester Project

Implementation of a MATLAB tool for multi-objective inverter optimization in the context of electric vehicles

  • ETH Zurich
  • Laboratory for High Power Electronic Systems

Evaluation of different inverter topologies and different modulation schemes using a multi-objective optimization procedure for a given target drive system. Optimization with respect to different objectives, e.g., efficiency, power density, costs. An existing toolchain provides a framework which can serve as a starting basis for your models and optimization algorithms. Analysis and validation of the design based on vehicle drive cycles.

  • Electrical Engineering
  • Master Thesis

Deep Learning for Pathology Applications

  • Harvard
  • Pathology

The Mahmood Lab (faisal.ai, Harvard Medical School, Brigham and Women's Hospital) seeks applications for master's students interested in developing deep learning methods for pathology image analysis. Our lab uses deep learning, data fusion, and medical image analysis to develop streamlined workflows for cancer diagnosis, prognosis, and biomarker discovery.

  • Artificial Intelligence and Signal and Image Processing
  • Internship, Master Thesis
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