Academic Positions

Description and lab research areas:

Our lab studies the health impacts of air pollution, the number one environmental cause of the global disease burden. This newly funded EU project ( ASVOLEE, Effects on Air quality of Semi-VOLatile Engine Emissions) involves studying cytotoxicity imposed by exposure to SOA particles formed from on-road cars under real driving conditions.

The project will use cultured cell models, together with big data analysis such as toxicology, RNA sequencing, and metabolomics analyses. Recent laboratory experience and basic molecular biology and biochemistry skills are an advantage. Through this new EU project, we will offer a dynamic and international collaboration between the Weizmann Institute of Science and other key laboratories in Europe. The work involves active collaborations between the participating groups and taking active roles in joint experimental campaigns, data analyses, paper writing, and weekly joint seminars, among other activities.

The target of this PhD/Post Doc research focuses on investigating the mechanisms (biological and toxicological effects) induced by collected SOA in field campaigns with advanced exposure models. In vitro exposure will focus on optimizing exposure of human epithelial lung cells and other tissue (liver, fat) cultures and developing differentiated 3D cell cultures and disease-oriented tissue models.

Required elements for research-based postdoc applications:  Recent laboratory experience and basic molecular biology and biochemistry skills are an advantage. PhD in Chemistry, Biology, Biochemistry, Atmospheric sciences, or related fields.

Please send applications to Prof. Yinon Rudich, Department of Earth and Planetary Sciences, Weizmann Institute, Rehovot 76100, Israel
e-mail: yinon.rudich@weizmann.ac.il, Tel: 972 8 934 4237

Lab website: https://www.weizmann.ac.il/EPS/Rudich/

Aerosols participate in many environmental processes affecting the climate, human health and ecosystems. The qualified candidate will work on topics related to organic aerosol aging, physical properties, and potential health effects. Our research efforts focus on: a) understanding the origin, molecular composition, atmospheric transformations, and optical properties of light-absorbing organic aerosols (anthropogenic, biogenic, urban, and biomass burning SOA), and b) connecting aerosol composition, its changes, and the resulting health effects. Some relevant papers:

Required elements for research-based postdoc applications

Ph.D. in chemistry, chemical or environmental engineering, with a solid background in atmospheric and analytical chemistry and technology. Strong publication record. Prior hands-on experience with aerosol analytical and physical chemistry. Expertise with some of the advanced analytical techniques for aerosol technology, such as HR- ToF - AMS, oxidation flow reactors (PAM), aerosol characterization and handling (such as SMPS, APS, CPC, flow control). Experience with analytical chemistry is advantageous (FTIR, Raman and UV-Vis spectroscopy). Additional requirements include practice with handling and interpretation of multi-modal data sets; complex data analysis, computer programming skills, experience in working in a research team environment, good oral and written communication skills, interpersonal skills, initiative, creative and innovative thinking.

Lab website: https://www.weizmann.ac.il/EPS/Rudich/

Contact information: Prof. Yinon Rudich, Department of Earth and Planetary Sciences, Weizmann Institute, Rehovot 76100, Israel
e-mail: yinon.rudich@weizmann.ac.il
Tel: 972 8 934 4237

Description and lab research areas:

Our lab studies the health impacts of air pollution, the number one environmental cause of the global disease burden. This newly funded EU project ( ASVOLEE, Effects on Air quality of Semi-VOLatile Engine Emissions) involves studying cytotoxicity imposed by exposure to SOA particles formed from on-road cars under real driving conditions.

The project will use cultured cell models, together with big data analysis such as toxicology, RNA sequencing, and metabolomics analyses. Recent laboratory experience and basic molecular biology and biochemistry skills are an advantage. Through this new EU project, we will offer a dynamic and international collaboration between the Weizmann Institute of Science and other key laboratories in Europe. The work involves active collaborations between the participating groups and taking active roles in joint experimental campaigns, data analyses, paper writing, and weekly joint seminars, among other activities.

The target of this PhD/Post Doc research focuses on investigating the mechanisms (biological and toxicological effects) induced by collected SOA in field campaigns with advanced exposure models. In vitro exposure will focus on optimizing exposure of human epithelial lung cells and other tissue (liver, fat) cultures and developing differentiated 3D cell cultures and disease-oriented tissue models.

Required elements for research-based postdoc applications:  Recent laboratory experience and basic molecular biology and biochemistry skills are an advantage. PhD in Chemistry, Biology, Biochemistry, Atmospheric sciences, or related fields.

Please send applications to Prof. Yinon Rudich, Department of Earth and Planetary Sciences, Weizmann Institute, Rehovot 76100, Israel
e-mail: yinon.rudich@weizmann.ac.il, Tel: 972 8 934 4237

Lab website: https://www.weizmann.ac.il/EPS/Rudich/

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:

• PhD in computer sciences/ physics/ environmental science/ engineering /statistics/ related fields
• Reach a theoretical understanding of deep learning and or statistical modeling.
• Proven experience in Python packages such as Scikit-learn, Pytorch, Tensorflow, etc. 
• Proven independence, self-management, and self-learning skills

Preferred Background in:

• Experience in the analysis of spatio-temporal data/ remote sensing/ monitoring networks.

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:

• PhD in computer sciences/ physics/ environmental science/ engineering /statistics/ related fields
• Reach a theoretical understanding of deep learning and or statistical modeling.
• Proven experience in Python packages such as Scikit-learn, Pytorch, Tensorflow, etc. 
• Proven independence, self-management, and self-learning skills

Preferred Background in:

• Experience in the analysis of spatio-temporal data/ remote sensing/ monitoring networks.

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.