20 Postdoctoral Fellowship at ETH Zurich, Switzerland

There is an urgent need to create sustainable and resilient value chains to support and speed up the energy transition. However, Switzerland and the EU are highly dependent on imported critical devices and raw materials – especially in strategic sectors, such as renewable energy. The project SPEED2ZERO of the joint initiative of the ETH domain aims to develop toolboxes, action plans, and technology to enable a sustainable transformation to a net zero greenhouse gas (GHG) and biodiversity-positive Switzerland. Solutions will target immediate action conducive to halving GHG emissions by 2030, developing the required infrastructure, ensuring a resilient energy system, and securing biodiversity. In the context of this project, the Reliability and Risk Engineering group will research transition pathways for the Swiss and European energy systems that are compatible with climate targets (i.e., net-zero GHG emissions by 2050) and will analyse factors that can hinder the roll-out of net zero pathways. These include global industry production capacity of renewable devices, policy-driven adoption rates, and propose measures that can accelerate the Swiss energy system transition to 2030.

We are part of the CREMA collaboration at PSI aiming at the measurement of the hyperfine splitting in muonic hydrogen (an atom formed by a muon and a proton) to investigate the proton structure and test fundamental theories such as quantum electrodynamics. The postdoctoral work will be carried out in a team formed by members of our ETH group, the Laboratory for Particle Physics at the Paul Scherrer Institute (PSI), and other partners.

The research group of Prof. Müller, Laboratory of Energy Science and Engineering at ETH Zürich (www.ese.mavt.ethz.ch; @GroupLese) is looking for a highly motivated candidate with a strong expertise and interest in the development of heterogeneous catalysts and their in-depth characterization. The position is founded by Innosuisse and the project is in close collaboration with an industrial partner.

Your main responsibility of you is to take a pivotal role in research collaborations with industrial partners. This includes the design of algorithms for challenging real-world problems, the efficient implementation of such algorithms, and also being the main contact person for some of our industrial research partners. The position provides excellent opportunities to build a strong profile on the interface of academic and industrial research. It strengthens and expands the applied profile of the group, which complements a strong existing theoretical research thrust, and comes with exciting opportunities to contribute and shape existing applied projects and also spawn new ones, typically in collaboration with industrial partners. The Combinatorial Optimization Group is part of the Institute for Operations Research within the Mathematics Department of ETH Zurich. We are a well-connected research group with strong international visibility. We offer a very stimulating work environment with a highly competitive salary and benefits, excellent working conditions and infrastructure in the centre of Zurich, and generous travel support. The position is initially for two years with the possibility of an extension. The starting date is flexible within 2023.

A postdoctoral research position is available in the Laboratory of Drug Formulation and Delivery, Department of Chemistry and Applied Biosciences, ETH Zurich (Switzerland). The primary focus of this project is to investigate the DNA delivery and transfection efficiency of novel protein-based DNA transfection systems.

At ETH Zurich, you will work in the Cosmology group of Prof. Alexandre Refregier and may focus on data analysis, observations and/or theoretical interpretation. The research group is involved in several wide-field state-of-the-art cosmological survey projects and has guaranteed access to high-performance computer clusters. The position is initially for one year, extendible to three years with attractive salaries. Postdocs have access to research funds for computers, travel, conference participation, publication charges and other needs.

You will be responsible for running a basic pulsed laser deposition system and fabricating high-quality thin films, nanocomposites and multilayers. Furthermore, you will carry out research that fits with one or more of the research fields of the Laboratory for Mesoscopic Systems including frustrated spin systems, ultrafast processes, hybrid systems and spintronics. In addition, you will be part of a team ascertaining the properties of the systems with large-scale facilities (synchrotron x-rays and neutrons).  In this postion, you will have the opportunity to create nanostructures with state-of-the-art nanofabrication methods, and study ultrafast processes with lab-based equipment and x-ray-free electron lasers. Finally, you are expected to make measurements and take responsibility for the group instruments such as the x-ray diffractometer, and SQUID-VSM and PPMS measurement systems.

In this role, you will be responsible for developing and implementing innovative algorithms and data structures to improve the use of long-read sequencing technologies for adaptive sequence sampling. Specifically, this concerns the development of algorithms to decide in real-time whether a DNA/RNA fragment being currently sequenced should be further considered or discarded. Ultimately, the project aims for solutions that are feasible to run on constrained hardware resources. You are expected to present your findings at conferences in the field and to draft manuscripts aimed for peer-reviewed publication. If you are passionate about bioinformatics, have a strong background in algorithm development and data structures, have programming expertise, and experience supervising students, we would love to hear from you!

We seek to establish mechanistic modelling in personalized medicine. Mechanistic modelling enables the integration of both prior knowledge about regulatory feedback and the physical-chemical constraints posed by diffusion-based transport into a computational framework. Unlike typical statistical models, mechanistic models represent the biomedical and clinical knowledge about the regulatory processes. Similar to clinicians in their daily work, such models can operate based on clinical datasets that are too small for machine-learning approaches. But unlike clinicians, the models can encompass more information, are more consistent, and can use mathematics to predict the non-linear dynamical behaviour based on the known regulatory interactions. In combination, mechanistic computational models are promising approaches to personalize and guide both patient data collection and treatments based on the few patient data points that can be routinely collected in the clinic.