Academic Positions

Our group studies clouds and their role in the climate system. We are interested in the dynamic and microphysical processes within clouds and cloud fields, cloud organization, atmospheric radiation transfer, and clouds' climatic trends. We are exploring the emergence of pattern formation within cloud fields using mathematical models and AI.

We develop analytical models, use numerical models, and analyze surface and satellite observations. Additionally, we are developing new AI approaches to study the emergence of patterns in self-organizing systems.

If you are interested in these topics and have a solid background in physics, geophysics, atmospheric sciences, and/or mathematics, you are welcome to contact us.

We are seeking a motivated PhD candidate for an interdisciplinary project focused on developing compact and sensitive optical instruments for measuring atmospheric trace gases. The project will center on broadband cavity-enhanced absorption spectroscopy, or BBCEAS, and its application to air-pollution research. The goal is to develop portable, robust instruments that can operate outside the laboratory and potentially be deployed on drones and other mobile platforms.

Research focus

The candidate will develop and improve a compact BBCEAS instrument, including its optical configuration, high-reflectivity cavity, light source, spectrometer, sampling system, electronics, calibration, and automated spectral analysis. An important direction will be adapting the instrument for drone deployment by reducing its size, weight, and power consumption while maintaining sensitivity and stability. Such measurements can provide vertical and spatial information on trace gases that conventional monitoring stations cannot capture. The project may also expand toward optical detection of additional atmospheric gases through the selection of new spectral regions, optical components, and multi-gas sensing approaches. The developed systems will be tested in the laboratory and deployed in atmospheric field campaigns.

Candidate profile

We are looking for a candidate with a background in physics, optical, electrical or mechanical engineering, atmospheric science, chemistry, or a related field. Experience with optics, spectroscopy, instrumentation, electronics, mechanical design, Python, signal processing, or drones is an advantage. The ideal candidate will have strong experimental and analytical skills, hands-on aptitude, attention to detail, and an interest in taking an instrument from laboratory development to real-world deployment. Good communication skills and the ability to work collaboratively in an interdisciplinary team are important.

Application

Please send a CV, a brief statement of research interests and relevant experience, and contact details for two references to yinon.rudich@weizmann.ac.il. Applications will be reviewed on a rolling basis until the position is filled.

We are seeking a motivated PhD candidate for an interdisciplinary project focused on developing advanced AI methods for high-resolution atmospheric forecasting.

Global weather models provide valuable large-scale predictions but often miss local variability, extreme events, and rapidly changing conditions. This project will combine numerical weather forecasts, satellite data, ground observations, and field measurements with deep-learning methods to improve predictions at local and regional scales.

Research focus

The candidate will develop transformer-based and other machine-learning models for atmospheric forecasting, downscaling, bias correction, and uncertainty estimation.

Possible applications include air-pollution episodes, dust transport, biomass burning, solar radiation, temperature, precipitation, and other environmental variables. The project will also explore interpretable and physically informed AI methods to understand what drives forecast performance and how well models generalize across locations and seasons. The work may include collaboration with international research groups and participation in measurement campaigns in Israel and abroad.

Candidate profile

We are looking for a candidate with a background in atmospheric science, physics, Earth sciences, engineering, computer science, applied mathematics, statistics, or a related field. Experience with Python, machine learning, deep learning, meteorological datasets, remote sensing, numerical modeling, or time-series analysis is an advantage. The ideal candidate will have strong analytical and programming skills, curiosity about atmospheric processes, and the ability to work collaboratively in an interdisciplinary team.

Application

Please send a CV, a brief statement of research interests and relevant experience, and contact details for two references to yinon.rudich@weizmann.ac.il. Applications will be reviewed on a rolling basis until the position is filled.

We are seeking a motivated PhD candidate for an interdisciplinary project focused on developing and deploying advanced drone-based systems for high-resolution measurements of urban and rural air pollution. Air-pollution concentrations can vary substantially over short distances due to traffic, emissions, buildings, vegetation, and atmospheric processes. Conventional monitoring stations often cannot capture this variability. The project will combine drone measurements (in Israel and abroad), mobile ground platforms, advanced instrumentation, and machine learning to map pollution in three dimensions and at high spatial and temporal resolution.

Research focus

A central part of the project will be the development of a field-ready drone measurement platform. The candidate will integrate lightweight sensors, sampling systems, positioning, data acquisition, and operational protocols into a robust system for atmospheric research. The platform will be deployed together with an instrumented mobile vehicle to investigate fine-scale pollution patterns near the ground and throughout the lower atmosphere. The project will also develop machine-learning methods for spatial reconstruction, analysis, and short-term prediction. Field campaigns may take place in Israel and abroad, including potential deployments across Africa. The candidate will participate in the full research process, from system design and laboratory testing to field deployment, data analysis, and scientific publication.

Candidate profile

We are looking for a candidate with a background in atmospheric science, physics, engineering, computer science, or a related field. Experience with field measurements, Python, deep learning, drones, electronics, or scientific instrumentation is an advantage. The ideal candidate will have strong analytical skills, hands-on technical aptitude, curiosity about atmospheric processes, and an interest in combining measurement systems with data-driven modeling. Good communication skills, initiative, and the ability to work effectively in an interdisciplinary team are essential.

Application

Please send a CV, a brief statement of research interests and relevant experience, and contact details for two references to yinon.rudich@weizmann.ac.il. Applications will be reviewed on a rolling basis until the position is filled.