11 Postdoctoral Fellowship at ETH Zurich, Switzerland

The Animal Nutrition Group, led by Prof. Mutian Niu at ETH Zurich’s Institute of Animal Sciences, focuses on advancing sustainable livestock production through innovative nutritional strategies. Our research integrates hypothesis-driven experiments with data-driven approaches to enhance nutrient utilization efficiency in ruminants, particularly dairy cattle. Key areas include feed and feeding, nutritional physiology, precision livestock farming, and multi-omics approaches toward sustainable dairy farming, leveraging advanced statistical modeling, machine learning, and AI to uncover biological insights and optimize production efficiency, animal health and environmental impact. We are seeking a talented researcher to join our dynamic, interdisciplinary team and drive forward our computational efforts in these domains.

We are looking for a highly motivated and collaborative researcher to join us in advancing these efforts. The candidate will work as part of a multidisciplinary team and be passionate about science, technology, collaboration, and communication. The candidate will work closely with laboratory members (experimental and computational) and engage in planning projects and experiments, developing computational methods, analyzing as well as integrating omics datasets, and interpreting findings.  

The candidate will primarily be engaged in the following activities:

• Develop analysis methods and advise on experimental design (target gene selection, power analyses, guide-library design, readout selection).
• Build, maintain, and document reproducible analysis pipelines (Python/R; Snakemake/Nextflow preferred) for novel experimental methods.
• Develop and apply methods for demultiplexing, normalization/QC, effect-size estimation, biological inference, and predictive modeling.
• Contribute to biological manuscripts and methods papers, present results within the lab and at conferences, and help mentor students.
• Use and maintain lab resources on HPC and Github. Tools you will use daily include Python and R, version control (Git), and HPC schedulers.

The Robotic Materials group is looking for three postdocs and one PhD for the project funded by ERC Starting Grant: “Distributed Addressable Robotic Material via Wavelength-Division-Multiplexing (ColorfulAct)”. ColorfulAct aims to establish the building blocks of physical intelligence parallel to nodes and connections to artifical intelligence. We will develop distributed and addressable robotic actuating materials that demostrate multifunctionality through the broad bandwidth of light.

The Robotic Materials group headed by Prof. Hedan Bai at Department of Materials was established in October 2023. We are an international and interdisciplinary team of material scientists, roboticists, engineers and chemists with a passion for futuristic robotics. We take a bottom-up approach to robotics and develop soft materials and devices that would enable unusual form and unconventional functions for broader robotic applications.

The goal of this project is to leverage advanced machine learning to develop an automated design process of mechanical walking aids, analyse gait patterns, and make biomechanical simulations embedded in the generative mechanism design process.

As a team member, we value an active role in integrating into our group, experiencing a range of exciting challenges, including the development of innovative technologies for patient usage and developing standardisation methods for customising aids.

In this project, we invite applicants interested in coordinating and leading a team of researchers. Coordinating with stakeholders and the research team to develop a new design process and additive manufacturing approach based on simulations of individual walking patterns. 

Quantum technologies promise to revolutionize multiple branches of science by solving problems that cannot be tackled by classical systems. While efficient and large-scale quantum computers are still far from being commercialized, quantum communication technologies are a reality already present on the market. The use of quantum states to share ultra-secure information is the only proven approach to achieve true communication privacy. However, un-conditional security requires the use of entanglement to establish non-classical correlations between communicating nodes, thus robust quantum networks and a future quantum internet. Commercial solutions avoid entanglement-based methods due to technical challenges, yet this limits the impact such technologies can have. In this project, we aim at further developing photonic integrated circuits for quantum communication, already demonstrated by our group, to address these performance limitations and realize integrated quantum transmitter and receiver of time-bin entangled photonic qubits. To do this, the lithium niobate on insulator (LNOI) platform holds a clear advantage over other integrated optics platforms as its high second order nonlinearity allows for efficient entangled qubits generation and high-speed signal modulation.

The project aims to develop quantum communication units on the LNOI platform, including qubit generation, transmission, measurement and drivers as a first prototype of entanglement-based high-speed photonic integrated circuits for quantum communication and networking. This includes developing high-speed electronic to drive the circuits, read and process data. The advertised position revolves around design and test of custom radio-frequency electronic circuits based on field-programmable gate arrays (FPGA, up to 20GHz) to drive high-speed electro-optic modulators, quasi-static thermo-optic qubit phase tuning, as well as process the transmitted quantum information in real time.