PhD position (f/m/x) – Climate extremes in tropical ecosystems - an assessment through data and models Helmholtz-Zentrum für Umweltforschung

PhD position (f/m/x) – Climate extremes in tropical ecosystems - an assessment through data and models Helmholtz-Zentrum für Umweltforschung - UFZ

https://recruitingapp-5128.de.umantis.com/Vacancies/2021/Description/2

The Helmholtz Centre for Environmental Research (UFZ) with its 1,100 employees has gained an excellent reputation as an international competence centre for environmental sciences. We are part of the largest scientific organisation in Germany, the Helmholtz association. Our mission: Our research seeks to find a balance between social development and the long-term protection of our natural resources.

The newly established Department of Remote Sensing in UFZ in tandem with the Remote Sensing Centre for Earth System Research (RSC4Earth.de) - a joint initiative of UFZ and the Faculty of Physics and Earth Sciences at Leipzig University - conducts innovative research to advance the understanding of the Earth system via the integration of various remote sensing, data science, and process-oriented modelling techniques. It has extensive research experience in quantifying land surface dynamics from multi-source Earth observations across scales.

Within the PhD framework "MoDEV - Towards novel model-data fusion for understanding environmental variability in space and time from high-resolution remote sensing" we are seeking to appoint a highly motivated candidate for the PhD project on "Climate extremes in tropical ecosystems - an assessment through data and models".

PhD position (f/m/x) – Climate extremes in tropical ecosystems - an assessment through data and models

Working time: 65% (25.35 hours per week), limited to 3 years

Your tasks:

This PhD project aims to understand the impact of extreme events (e.g. drought, heat wave) on ecosystem dynamics in the tropics. We are interested in understanding the role of land-surface dynamics on different ecosystems based on satellite observations and model simulations. Key research questions include:

What are the spatiotemporal dynamics of climate-induced extreme events in tropical forests and how do their impacts and characteristics change regionally as a function of environmental conditions?

What is the role of species and structural diversity in buffering the impacts of climate extremes; can we describe gradients of resilience within tropical forest ecosystems?

To achieve this goal, optimal data-fusion strategies for merging satellite remote sensing data and models with different spatial and temporal resolutions shall be developed

The PhD project will be supervised by Prof. Miguel Mahecha (UFZ and Leipzig University) and Prof. Andreas Huth (UFZ).

Your profile:

Master degree (or equivalent) in Earth system (data-) science, computer sciences, remote sensing, hydrology, meteorology, applied mathematics, or any related field.

Fluency in one language of scientific computing (e.g., Julia, Python, Fortran, R)

Ideally a solid background in either machine-learning or dynamical modelling

Genuine interest in understanding how the Earth system works!

Willingness to publish results in peer-reviewed journals and present at scientific meetings

Good communication skills in English, and strong interest to work in an interdisciplinary research team

We offer:

Excellent technical facilities which are without parallel

The freedom you need to bridge the difficult gap between basic research and close to being ready for application

Work in interdisciplinary, multinational teams

Excellent links with national and international research networks

Excellent support and optimal subject-specific and general training with our HIGRADE graduate school

Remuneration in accordance with the TVöD public-sector pay grade 13 (65%)

Vineesh V
Assistant Professor of Geography,
Directorate of Education,
Government of Kerala.
https://www.facebook.com/Applied.Geography
http://geogisgeo.blogspot.com

Comments

Atmospheric Window

The atmospheric window in remote sensing refers to specific wavelength ranges within the electromagnetic spectrum that can pass through the Earth's atmosphere relatively unimpeded. These windows are crucial for remote sensing applications because they allow us to observe the Earth's surface and atmosphere without significant interference from the atmosphere's constituents. Key facts and concepts about atmospheric windows: Visible and Near-Infrared (VNIR) window: This window encompasses wavelengths from approximately 0. 4 to 1. 0 micrometers. It is ideal for observing vegetation, water bodies, and land cover types. Shortwave Infrared (SWIR) window: This window covers wavelengths from approximately 1. 0 to 3. 0 micrometers. It is particularly useful for detecting minerals, water content, and vegetation health. Mid-Infrared (MIR) window: This window spans wavelengths from approximately 3. 0 to 8. 0 micrometers. It is valuable for identifying various materials, incl

DRA Disaster Risk Assessment

Disaster Risk Assessment (DRA): A Professional Overview Disaster Risk Assessment (DRA) is a systematic process used to identify, analyze, and evaluate the potential hazards, vulnerabilities, and risks posed by disasters to people, property, infrastructure, and the environment. It is a critical tool for effective disaster risk management, enabling communities, organizations, and governments to make informed decisions and implement appropriate mitigation measures. Key Components of DRA Hazard Identification: Identifying the types of hazards that could potentially affect a specific area, such as natural disasters (earthquakes, floods, cyclones), technological disasters (industrial accidents, infrastructure failures), or man-made disasters (conflicts, pandemics). Vulnerability Assessment: Evaluating the susceptibility of people, infrastructure, and the environment to the identified hazards. This involves assessing factors such as location, construction quality, socio-economic co

Linear Arrays Along-Track Scanners or Pushbroom Scanners

Multispectral Imaging Using Linear Arrays (Along-Track Scanners or Pushbroom Scanners) Multispectral Imaging: As previously defined, this involves capturing images using multiple sensors that are sensitive to different wavelengths of electromagnetic radiation. Linear Array of Detectors (A): This refers to a row of discrete detectors arranged in a straight line. Each detector is responsible for measuring the radiation within a specific wavelength band. Focal Plane (B): This is the plane where the image is formed by the lens system. It is the location where the detectors are placed to capture the focused image. Formed by Lens Systems (C): The lens system is responsible for collecting and focusing the incoming radiation onto the focal plane. It acts like a camera lens, creating a sharp image of the scene. Ground Resolution Cell (D): As previously defined, this is the smallest area on the ground that can be resolved by a remote sensing sensor. In the case of linear array scanne

Discrete Detectors and Scanning mirrors Across the track scanner Whisk broom scanner.

Multispectral Imaging Using Discrete Detectors and Scanning Mirrors (Across-Track Scanner or Whisk Broom Scanner) Multispectral Imaging: This technique involves capturing images of the Earth's surface using multiple sensors that are sensitive to different wavelengths of electromagnetic radiation. This allows for the identification of various features and materials based on their spectral signatures. Discrete Detectors: These are individual sensors that are arranged in a linear or array configuration. Each detector is responsible for measuring the radiation within a specific wavelength band. Scanning Mirrors: These are optical components that are used to deflect the incoming radiation onto the discrete detectors. By moving the mirrors, the sensor can scan across the scene, capturing data from different points. Across-Track Scanner or Whisk Broom Scanner: This refers to the scanning mechanism where the mirror moves perpendicular to the direction of flight. This allows for t

Hazard Vulnerability Exposure Risk

Key Concepts in Hazard Identification, Vulnerability Assessment, Exposure Assessment, and Risk Analysis Hazard-Exposure-Vulnerability-Risk (HEVR) Framework: Hazard: A potential event or phenomenon that can cause harm. Exposure: People, assets, or environments in harm's way. Vulnerability: Susceptibility to damage or harm from a hazard. Risk: The potential for loss or damage resulting from the interaction of hazards, exposure, and vulnerability. Risk as a Function: Risk can be calculated using the formula: Risk = Hazard × Vulnerability × Exposure. Reducing any of these factors can decrease overall risk. Types of Hazards: Natural hazards: Earthquakes, floods, tsunamis, landslides, hurricanes. Anthropogenic hazards: Industrial accidents, pollution, infrastructure failure, climate change. Technological hazards: Nuclear accidents, chemical spills. Vulnerability Dimensions: Physical: Infrastructure quality, building codes, location. Social: Age, income, disability, gender, acces

Vineesh V, Geography

Search This Blog