Student Projects

The student work will pick up from two previous student projects that performed snowfall measurements using a commercial DJI Mavic 3E drone with tele-lens and search-and-rescue spotlight, and developed a novel DJI Matrice 600 Pro microscopy platform with pulsed illumination and automated to capture high-resolution snowflake imagery in freefall. The current project will combine these platforms to perform atmospheric profiling flights in the Swiss Alps during the 2025/2026 snow season. The goal will be to harvest invaluable snowflake data that can be compared with ground- and balloon-based literature. At the start of the research, the student will familiarize with the onboard flight and imaging systems for both platforms. The student will practice automated flight path planning at a designated football field in Zurich area and build up a reference dataset of laboratory snowflake images under known temperature, humidity, and possibly riming conditions. The student will also be involved in streamlining both systems' design, various onboard image acquisition, boosting the data-acquisition rate, and integrating existing temperature, humidity, and ambient flow sensors for metadata collection. In the second stage of the research, the student will deploy the UAV systems outdoors during snowfall events at a selected number of field measurement sites in Davos. The profiling flight path parameters will need to be planned such that the flight operation is fully reproducible for repeated measurements. The measurement campaigns will build up large snowflake datasets that will be paired and cross-validated with on-site meteorological towers. During this part of the research, the student will leverage fieldwork in a team format with several involved parties and gain experience practicing strong planning and communication skills. In the final stage, the acquired data will be analyzed. The student will need to extract and interpret statistical distributions of snowflake aspect ratio, orientation angle, and complexity, and assess the analysis for various available image processing routines. Furthermore, the student will need to compare these analyses against the temperature, humidity, and estimates of turbulence kinetic energy. This will allow a full statistical characterization of the snowflake morphological variety and free-falling behavior. The student will need to map these data against the height of the air column and compare them against the existing literature.

The goal of this project is to acquire invaluable amounts of snowflake image data in freefall using two UAV-based systems for atmospheric profiling. Performing such field experimental work is a transformation and novel development in fluid dynamics. These image data will help us gain new insight into the settling dynamics of natural snowfall in the atmosphere, which are not accessible in the lab. This project contributes to a broader commitment to studying snowfall in collaboration between the ETH Zurich Institute of Fluid Dynamics (IFD-ETH) and the Snow and Avalanche Institute (WSL-SLF) in Davos.

You will work with members from the Institute of Fluid Dynamics in the Department of Mechanical and Process Engineering (D-MAVT) and interact with members of the Robotics and Perception Group (RPG-UZH). To apply, please send your full transcript (up to the current semester) and a short motivation letter to kmuller@ethz.ch, bauersfeld@ifi.uzh.ch, and fcoletti@ethz.ch. A strong interest and relevant classes in fluid dynamics, signal processing, and robotics are preferred. The project is planned to start in Fall 2025 and is open to bachelor’s and master’s students. See exp.ethz.ch/research/field-measurements for more information.

To perform outdoor volumetric imaging, the student will be using a newly developed three-dimensional field imaging setup at the Institute of Fluid Dynamics. This setup encompasses 16 high-resolution cameras distributed over multiple camera arrays that can be deployed in ice-fishing tents, and which are calibrated by a custom-designed drone-based flying calibration target over a pre-programmed flight path. The student will co-locate this system with a dedicated snow holography setup and pair it with meteorological data. These activities will define non-existent data sets from field imaging to characterize snow clustering in turbulence. In the first stage of the research, we will perform experimental work in the early 2025/2026 snow season. The student will be deploying our imaging setup at the Weissfluhjoch instrumented field site (2600m) at the Snow and Avalanche Institute in Davos. During this part of the research, the student will leverage fieldwork in a team format, allowing to practice strong planning and communication skills. The student will implement a pulsed illumination system for volumetric imaging of snowflakes over a large depth of field, assess different pulsed illumination configurations, and compare them with an existing continuous illumination setup. In the second stage of the research, the student will have a unique chance to visit the DLR in Göttingen and process different samples of snow tracking data using the state-of-the-art ‘Shake-the-Box’ particle tracking algorithm. The student will perform quantitative assessments of the tracking performance and accuracy in relation to the camera calibration flights and the system's design. The student will compare results for different illumination conditions and compare against in-house snowfall reconstruction techniques. The student will identify possible sources of measurement bias and make several recommendations for future improvements. In the final stage of the research, the student will perform quantitative data analysis of snowfall tracking and perform a Voronoi analysis to identify snow clusters (and voids) over length scales not accessible before. The student will map these analyses against on-site meteorological data to identify the different snowfall and flow regimes. The student will compare the three-dimensional nature of the snow-particle turbulence interaction against existing two-dimensional studies. The innovative character of this project will provide unlimited ground to practice and excel in strong problem-solving skills and foster a high degree of creativity.

The goal of this project is to perform large-scale volumetric imaging of natural snowfalls in the field. Performing such field experimental work is a transformational novel development in fluid dynamics. These imaging data and analysis will help elucidate the different types of coupling between natural snowfall and atmospheric turbulence. The student will start a new collaboration with the German Aerospace Center in Göttingen (DLR). This project will be a central contribution to studying snowfall between the ETH Zurich Institute of Fluid Dynamics (IFD-ETH) and the Snow and Avalanche Institute (WSL-SLF) in Davos.

You will work with members from the Institute of Fluid Dynamics in the Department of Mechanical and Process Engineering (D-MAVT) and collaborate with members of the Experimental Methods Group (PIV/STB) at the DLR in Göttingen. To apply, send your full transcript (up to the current semester) and a short motivation letter to kmuller@ethz.ch, and fcoletti@ethz.ch. A strong interest and relevant classes in fluid dynamics, experimental methods, and imaging are preferred. The project can already start in Summer 2025 and is open to bachelor’s and master’s students. See exp.ethz.ch/research/field-measurements for more information.

To perform outdoor planar imaging, the student will be using a newly developed two-dimensional field imaging setup at the Institute of Fluid Dynamics. This setup includes 16 high-resolution cameras distributed over multiple camera arrays that can be deployed in ice-fishing tents and are calibrated using multiple drone flights. The student will be involved in co-locating this system with a dedicated holography setup for snow characterization. In addition, the student will be pairing the measurements with meteorological data and (high-frequency) eddy-covariance measurements. In the first stage of the research, the student will familiarize with the measurement equipment and will join in mountain measurement campaigns to practice field deployment and camera alignment. The student will help to improve a flight path strategy for maximum stability in the presence of the flight controller's response to external wind conditions. The student will also be involved in pulsing our field illumination system for planar imaging. In parallel, the student will work on the scope of the project to generate spacetime diagrams of various streamwise quantities (e.g., Voronoi tessellation) on existing data. In the second stage, estimated from November to the end of February, the student will be deploying the imaging setup at a designated field site at Tschuggen in Davos. This measurement field is located in a valley and has well-defined flow conditions, and co-locates eddy covariance measurement with other meteorological equipment. In addition to the field imaging setup, the student will deploy and operate a digital holography setup. The student will be involved in coordinating the field measurement campaigns and will gain experience being a strong team player and communicating with several involved parties. In the final stage of the research, the student will be mapping the snowfall tracking data against the holography measurements and co-located weather data. This will allow the student to identify various snowfall and flow regimes. The student will be applying the metrics studied at the start of the project to the newly acquired data. The student will also need to apply the knowledge of the actual field experiments to make critical and sharp observations on the data and compare them to existing literature. The innovative character of this project will enable the student to demonstrate a high degree of creativity.

The goal of this project is to perform large-scale planar imaging of natural snowfalls in the field. Performing such field experimental work is a transformational development in fluid dynamics. These imaging data and analysis will help elucidate the different types of coupling between natural snowfall and atmospheric turbulence. This project will run in parallel with a three-dimensional study, which is more technically involved. This project contributes to a broader commitment to studying snowfalls in collaboration between the ETH Zurich Institute of Fluid Dynamics (IFD-ETH) and the Snow and Avalanche Institute (WSL-SLF) in Davos.

You will work with members from the Institute of Fluid Dynamics in the Department of Mechanical and Process Engineering (D-MAVT) and collaborate with members of the Lohmann Cloud Physics Group (AP/ENV-ETH) at the ETH Zurich. To apply, send your full transcript (up to the current semester) and a short motivation letter to kmuller@ethz.ch, and fcoletti@ethz.ch. A strong interest and relevant classes in fluid dynamics, imaging, and snow physics are preferred. The project will start in Spring 2025 and is open to bachelor’s and master’s students. See exp.ethz.ch/research/field-measurements for more information.