Available Positions

Studying ice nucleation in clouds using a new microfluidics setup

Area: 
Chemistry
Physics
Tuesday, May 1, 2018

Investigating the health effects of urban pollution

Area: 
Chemistry
Life Sciences
Tuesday, May 1, 2018

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

Atmospheric microbiome: understanding the transport of microorganisms in the atmosphere.

Area: 
Life Sciences
Thursday, November 1, 2018

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.

Chemistry and properties of secondary organic aerosols

Area: 
Chemistry
Thursday, November 1, 2018

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

Atmospheric microbiome: understanding the transport of microorganisms in the atmosphere.

Area: 
Life Sciences
Friday, November 1, 2019

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.

Atmospheric microbiome: understanding the transport of microorganisms in the atmosphere.

Area: 
Chemistry
Friday, November 1, 2019

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.

Developing a portable drone for measuring major pollution components

Area: 
Chemistry
Wednesday, January 1, 2020

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.

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

Area: 
Chemistry
Physics
Sunday, June 2, 2024

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

Studying the Microbiome of the atmosphere.

Area: 
Chemistry
Life Sciences
Sunday, June 2, 2024

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

Studying the Microbiome of the atmosphere.

Area: 
Chemistry
Life Sciences
Sunday, June 2, 2024

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

Developing AI architectures for extreme weather events forecasting. Department of Earth and Planetary Science, Weizmann Institute of Science, Israel

Area: 
Chemistry
Mathematics and Computer Science
Physics
Sunday, June 2, 2024

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