Available Positions

The World Health Organization and the Global Burden of Disease Assessment concluded recently that exposure to ambient particulate pollution is the most important environmental health risk to people globally. Mechanistic understanding of the effect of particulate matter on lung inflammation is lacking. Today, all particulate pollution is assumed to have the same risk, although they are produced from different sources and chemical composition. The goal of this study is to reach an in-depth mechanistic understanding of how PM, and its chemical composition, affect human health through a combination of detailed chemical speciation and controlled laboratory PM exposure experiments. Overall, this study is expected to provide an essential link between exposure to particulate air pollution and increase in the onset of oxidative stress and inflammation, thereby providing a mechanistic quantitative link between environmental exposure to the increased risk of diseases. The combination of state of the science atmospheric chemistry with most advanced biological investigation presents a unique multidisciplinary new approach to address this important challenge.
Knowledge in biochemistry or chemistry is an andvantage

We use oxidation flow reactor to study the processing of aerosols and how they change their optical properties. Tools used are an aerosol mass spectrometer, broadband cavity enhanced spectrometers and others.
Excellent projects for postdocs with a background in aerosol optics, spectroscopy, mass spectrometry, and physical chemistry

Aerobiology: we study the transport of various pathogen, bacteria, and viruses by dust and winds. We use deep sequencing and bioinformatics to learn about the population of microorganisms in the air. Using state of the art sampling and RNA analysis we also study about functions and viability.

https://jobs.sciencecareers.org/job/490949/6-phd-student-2-postdoctoral-...

Aerobiology: we study the transport of various pathogen, bacteria, and viruses by dust and winds. We use deep sequencing and bioinformatics to learn about the population of microorganisms in the air. Using state of the art sampling and RNA analysis we also study about functions and viability.

Aerobiology: we study the transport of various pathogen, bacteria, and viruses by dust and winds. We use deep sequencing and bioinformatics to learn about the population of microorganisms in the air. Using state of the art sampling and RNA analysis we also study about functions and viability.

The successful candidate will develop and test a new and innovative sampling platform that will take advantage of a new generation of lightweight and mobile sensors mounted on a high-end drone with extreme maneuverability in three dimensions. We will then deploy it in several environments to measure the heterogeneous distribution of aerosols and gas phase co-pollutants.

PhD student position: Developing AI architectures for extreme weather events forecasting.

Department of Earth and Planetary Science, Weizmann Institute of Science, Israel

Responsibilities:

Develop and explore AI architectures for extreme weather events forecasting, driven by remote sensing and in-situ data, to replace theory-driven climate models.

Explore explainable AI approaches to gain a scientific understanding of the weather events' precursor processes and their physical patterns.

Identify and define unique challenges for AI in the field of remote sensing-driven extreme weather forecast models, and study novel solutions.

Explore the integration of fundamental physical and atmospherical theory (e.g., Navier–Stokes equations) within deep learning architectures.

Study unsupervised approaches for learning concise representations of large-scale spatio-temporal meteorological data sequences for various tasks, including memory compression, clustering, augmentation, and generative purposes.

The candidate is expected to advance the group's current AI capabilities and to be a source of knowledge for various machine learning and data science tasks carried out by other group members, including R&D projects of a drone-based system. Candidates should be passionate about Earth and planetary sciences, working in a small research team, and collaborating with researchers from other disciplines.

Minimum Qualifications:

• MSc in computer sciences/ physics/ environmental science/ engineering /statistics/ related fields
• Proven experience (theory and hands-on) in Statistical modeling/ machine learning / deep learning.
• Experience in developing research projects, from data acquisition through analysis to prediction.
• Proven independence, self-management, and self-learning skills
• Proven teamwork skills

Preferred Background in:

• Experience with Python packages such as Scikit-learn, Pytorch, Tensorflow, etc. 
• Experience in the analysis of spatio-temporal data/ remote-sensing/ 
• monitoring networks.
• In-depth understanding of deep learning theory

The atmospheric transport of microorganisms can affect the biodiversity and health of global ecosystems. However, the processes influencing airborne bacterial communities' abundance, composition, and dispersal are still not well understood. We study the aerial microbiome to better understand the structure, function, and ecological drivers of airborne communities transported by dust-plumes in the Eastern Mediterranean. We use state-of-the-art aerosol sampling techniques, Next-generation sequencing (NGS), molecular biology and bioinformatics tools.

We are looking for highly motivated and curious PhD students and PostDocs to join our team.

Required qualifications:

• MSc. or PhD. degree in microbial ecology, environmental genomics or related fields.
• Experience in DNA/RNA extraction techniques.
• Experience in bioinformatic/biostatistical pipelines using R or Python.
• Knowledge on the analysis and interpretation of microbial community genomics data.

The following additional qualifications will be advantageous:

• Background in bioaerosol research or related fields.
• Knowledge on molecular biology and microbiology techniques (i.e., genomic sequencing, qPCR, flow cytometry, cell culturing).
• The generation of NGS sequencing libraries.

Please contact:

Prof. Yinon Rudich

yinon.rudich@weizmann.ac.il

Department of Earth and Planetary Sciences

Weizmann Institute of Science

The atmospheric transport of microorganisms can affect the biodiversity and health of global ecosystems. However, the processes influencing airborne bacterial communities' abundance, composition, and dispersal are still not well understood. We study the aerial microbiome to better understand the structure, function, and ecological drivers of airborne communities transported by dust-plumes in the Eastern Mediterranean. We use state-of-the-art aerosol sampling techniques, Next-generation sequencing (NGS), molecular biology and bioinformatics tools.

We are looking for highly motivated and curious PhD students and PostDocs to join our team.

Required qualifications:

• MSc. or PhD. degree in microbial ecology, environmental genomics or related fields.
• Experience in DNA/RNA extraction techniques.
• Experience in bioinformatic/biostatistical pipelines using R or Python.
• Knowledge on the analysis and interpretation of microbial community genomics data.

The following additional qualifications will be advantageous:

• Background in bioaerosol research or related fields.
• Knowledge on molecular biology and microbiology techniques (i.e., genomic sequencing, qPCR, flow cytometry, cell culturing).
• The generation of NGS sequencing libraries.

Please contact:

Prof. Yinon Rudich

yinon.rudich@weizmann.ac.il

Department of Earth and Planetary Sciences

Weizmann Institute of Science

Develop and explore AI architectures for extreme weather events forecasting, driven by remote sensing and in-situ data, to replace theory-driven climate models.

Explore explainable AI approaches to gain a scientific understanding of the weather events' precursor processes and their physical patterns.

Identify and define unique challenges for AI in the field of remote sensing-driven extreme weather forecast models, and study novel solutions.

Explore the integration of fundamental physical and atmospherical theory (e.g., Navier–Stokes equations) within deep learning architectures.

Study unsupervised approaches for learning concise representations of large-scale spatio-temporal meteorological data sequences for various tasks, including memory compression, clustering, augmentation, and generative purposes.

The candidate is expected to advance the group's current AI capabilities and to be a source of knowledge for various machine learning and data science tasks carried out by other group members, including R&D projects of a drone-based system. Candidates should be passionate about Earth and planetary sciences, working in a small research team, and collaborating with researchers from other disciplines.

Minimum Qualifications:

• MSc in computer sciences/ physics/ environmental science/ engineering /statistics/ related fields
• Proven experience (theory and hands-on) in Statistical modeling/ machine learning / deep learning.
• Experience in developing research projects, from data acquisition through analysis to prediction.
• Proven independence, self-management, and self-learning skills
• Proven teamwork skills

Preferred Background in:

• Experience with Python packages such as Scikit-learn, Pytorch, Tensorflow, etc. 
• Experience in the analysis of spatiotemporal data/remote sensing/ 
• monitoring networks.
• In-depth understanding of deep learning theory

We are seeking a motivated PhD candidate to lead a research project on hyper-resolution measurement and modeling of urban air pollution, combining drone-based measurements, an instrumented mobile platform, advanced sensing, and deep-learning methods to study how pollution varies across cities at fine spatial and temporal scales.

Urban air pollution is highly heterogeneous: concentrations can change substantially over tens of meters due to traffic, buildings, vegetation, emissions, and atmospheric mixing, and conventional monitoring networks often miss this variability. This project aims to fill that gap by developing mobile and airborne approaches that produce detailed three-dimensional views of pollutant distributions in real urban environments, and by connecting these measurements with transformer-based deep-learning models for improved interpretation, mapping, and forecasting.

Research directions

The candidate will develop and deploy a drone-based atmospheric measurement system for high-resolution mapping of urban air pollution. A major part of the project focuses on advancing the platform itself, including the integration of lightweight sensors, optical instrumentation, sampling systems and ground-based mobile measurements into a robust field-ready system.

Using drones together with an instrumented mobile platform, the candidate will conduct field campaigns to collect high-resolution data across urban environments. These measurements will reconstruct fine-scale pollution patterns near the ground and throughout the lower urban boundary layer, capturing variability that conventional networks cannot resolve. The project will examine how concentrations vary across urban settings, how local features shape pollution patterns, and how well models trained in one environment generalize to others. An important direction is deployment in diverse environments, including international campaigns and potential measurements across Africa, where high-resolution air-pollution data are often limited but urgently needed.

The work also includes developing machine-learning methods for spatial reconstruction, analysis, and short-term prediction of pollution fields. By combining advanced instrumentation, drone and mobile sensing, and deep-learning models, the project aims to create a flexible platform for studying air pollution in complex real-world environments.

Responsibilities

• Design and carry out drone and mobile-platform field campaigns
• Develop and integrate the measurement system, including portable optical sensing and instrumentation
• Develop methods for hyper-resolution mapping of urban air pollution
• Analyze spatial and temporal pollutant variability in real environments
• Develop AI models for reconstruction, interpretation, and forecasting
• Investigate model generalization across measurement settings

Requirements

We are looking for a candidate with a strong background in either atmospheric science, physics, engineering, computer science, or a related field. Experience with field measurements, atmospheric data analysis, Python, deep learning, drones, or instrumentation is an advantage. The ideal candidate will have strong analytical skills, hands-on aptitude, curiosity about real-world atmospheric processes, and an interest in combining measurement systems with modern data-driven modeling. Strong communication skills, a collaborative mindset, and the ability to work effectively as part of an interdisciplinary team are also important.

To apply

Please send a CV and a brief statement of research interests to yinon.rudich@weizmann.ac.il

Develop and explore AI architectures for extreme weather events forecasting, driven by remote sensing and in-situ data, to replace theory-driven climate models.

Explore explainable AI approaches to gain a scientific understanding of the weather events' precursor processes and their physical patterns.

Identify and define unique challenges for AI in the field of remote sensing-driven extreme weather forecast models, and study novel solutions.

Explore the integration of fundamental physical and atmospherical theory (e.g., Navier–Stokes equations) within deep learning architectures.

Study unsupervised approaches for learning concise representations of large-scale spatio-temporal meteorological data sequences for various tasks, including memory compression, clustering, augmentation, and generative purposes.

The candidate is expected to advance the group's current AI capabilities and to be a source of knowledge for various machine learning and data science tasks carried out by other group members, including R&D projects of a drone-based system. Candidates should be passionate about Earth and planetary sciences, working in a small research team, and collaborating with researchers from other disciplines.

Group: Prof. Yinon Rudich, Department of Earth and Planetary Sciences

We are seeking a motivated PhD candidate to lead a research project on hyper-resolution measurement and modeling of urban air pollution, combining drone-based measurements, an instrumented mobile platform, advanced sensing, and deep-learning methods to study how pollution varies across cities at fine spatial and temporal scales.

Urban air pollution is highly heterogeneous: concentrations can change substantially over tens of meters due to traffic, buildings, vegetation, emissions, and atmospheric mixing, and conventional monitoring networks often miss this variability. This project aims to fill that gap by developing mobile and airborne approaches that produce detailed three-dimensional views of pollutant distributions in real urban environments, and by connecting these measurements with transformer-based deep-learning models for improved interpretation, mapping, and forecasting.

Research directions

The candidate will develop and deploy a drone-based atmospheric measurement system for high-resolution mapping of urban air pollution. A major part of the project focuses on advancing the platform itself, including the integration of lightweight sensors, optical instrumentation, sampling systems and ground-based mobile measurements into a robust field-ready system.

Using drones together with an instrumented mobile platform, the candidate will conduct field campaigns to collect high-resolution data across urban environments. These measurements will reconstruct fine-scale pollution patterns near the ground and throughout the lower urban boundary layer, capturing variability that conventional networks cannot resolve. The project will examine how concentrations vary across urban settings, how local features shape pollution patterns, and how well models trained in one environment generalize to others. An important direction is deployment in diverse environments, including international campaigns and potential measurements across Africa, where high-resolution air-pollution data are often limited but urgently needed.

The work also includes developing machine-learning methods for spatial reconstruction, analysis, and short-term prediction of pollution fields. By combining advanced instrumentation, drone and mobile sensing, and deep-learning models, the project aims to create a flexible platform for studying air pollution in complex real-world environments.

Responsibilities

• Design and carry out drone and mobile-platform field campaigns
• Develop and integrate the measurement system, including portable optical sensing and instrumentation
• Develop methods for hyper-resolution mapping of urban air pollution
• Analyze spatial and temporal pollutant variability in real environments
• Develop AI models for reconstruction, interpretation, and forecasting
• Investigate model generalization across measurement settings

Requirements

We are looking for a candidate with a strong background in either atmospheric science, physics, engineering, computer science, or a related field. Experience with field measurements, atmospheric data analysis, Python, deep learning, drones, or instrumentation is an advantage. The ideal candidate will have strong analytical skills, hands-on aptitude, curiosity about real-world atmospheric processes, and an interest in combining measurement systems with modern data-driven modeling. Strong communication skills, a collaborative mindset, and the ability to work effectively as part of an interdisciplinary team are also important.

To apply

Please send a CV and a brief statement of research interests to yinon.rudich@weizmann.ac.il

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.

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.