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

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

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.