Skip to main content

PhD position– Remote Sensing of soil moisture dynamics (f/m/x) Helmholtz-Zentrum für Umweltforschung - UFZ






PhD position– Remote Sensing of soil moisture dynamics (f/m/x) Helmholtz-Zentrum für Umweltforschung - UFZ


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) - 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 "Remote Sensing of soil moisture dynamics".
PhD position– Remote Sensing of soil moisture dynamics (f/m/x)
Working time 65% (25.35 hours per week), limited for 3 years
Your tasks:
The PhD project aims to synergize multi-source remote sensing observations to retrieve surface soil moisture dynamics with quantified uncertainties across scales. Robust methods will be developed to estimate root zone soil moisture via the integration of the retrieved surface soil moisture, land surface model and Cosmic-Ray Neutron Sensing (CRNS). Key research questions include:
Will the synergy of multi-source remote sensing data lead to a more accurate soil moisture estimate?
How can we tackle the scale mismatch in comparison of soil moisture from remote sensing, CRNS, and land surface modelling?
How to best integrate satellite surface soil moisture, model simulation, and filed soil moisture to obtain root zone soil moisture dynamics?
Your profile:
Master degree (or equivalent) in earth system science, remote sensing, meteorology, hydrology, physical geography, environmental sciences, physics, statistics, computer sciences, or a related field
Good programming skills (e.g., Python, Fortran, R, or Matlab)
Interest in microwave remote sensing and radiative transfer modelling
Willing 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

Post a Comment

Popular posts from this blog

Geologic and tectonic framework of the Indian shield

  Major Terms and Regions Explained 1. Indian Shield The Indian Shield refers to the ancient, stable core of the Indian Plate made of hard crystalline rocks. It comprises Archean to Proterozoic rocks that have remained tectonically stable over billions of years. Important Geological Features and Regions ▪️ Ch – Chhattisgarh Basin A sedimentary basin part of the Bastar Craton . Contains rocks of Proterozoic age , mainly sedimentary. Important for understanding the evolution of central India. ▪️ CIS – Central Indian Shear Zone A major tectonic shear zone , separating the Bundelkhand and Bastar cratons . It records intense deformation and metamorphism . Acts as a suture zone , marking ancient tectonic collisions. ▪️ GR – Godavari Rift A rift valley formed due to stretching and thinning of the Earth's crust. Associated with sedimentary basins and hydrocarbon resources . ▪️ M – Madras Block An Archean crustal block in...
Post a Comment
Read more

Geology and Tectonic. Indian Shield

1. Ch (Chattisgarh Basin): Chattisgarh Basin is a geological region in central India known for its sedimentary rock formations. It's important for its mineral resources, including coal and iron ore. 2. CIS (Central Indian Shear Zone): CIS is a tectonic boundary in central India where the Indian Plate interacts with the Eurasian Plate. It's characterized by significant faulting and seismic activity. 3. GR (Godavari Rift): The Godavari Rift is a geological feature associated with the rifting and splitting of the Indian Plate. It's located in the Godavari River basin in southeastern India. 4. M (Madras Block): The Madras Block is a stable continental block in southern India. It's part of the Indian Plate and is not associated with active tectonic processes. 5. Mk (Malanjkhand): Malanjkhand is known for its copper deposits and is one of the largest copper mines in India. 6. MR (Mahanadi Rift): The Mahanadi Rift is a geological feature related to the rifting of the Indian Pl...
Post a Comment
Read more

Evaluation and Characteristics of Himalayas

Time Period Event / Process Geological Evidence Key Terms & Concepts Late Precambrian – Palaeozoic (>541 Ma – ~250 Ma) India part of Gondwana , north bordered by Cimmerian Superterranes, separated from Eurasia by Paleo-Tethys Ocean . Pan-African granitic intrusions (~500 Ma), unconformity between Ordovician conglomerates & Cambrian sediments. Gondwana, Paleo-Tethys Ocean, Pan-African orogeny, unconformity, granitic intrusions, Cimmerian Superterranes. Early Carboniferous – Early Permian (~359 – 272 Ma) Rifting between India & Cimmerian Superterranes → Neotethys Ocean formation. Rift-related sediments, passive margin sequences. Rifting, Neotethys Ocean, passive continental margin. Norian (210 Ma) – Callovian (160–155 Ma) Gondwana split into East & West; India part of East Gondwana with Australia & Antarctica. Rift basins, oceanic crust formation. Continental breakup, East Gondwana, West Gondwana, oceanic crust. Early Cretaceous (130–125 Ma) India broke fr...
Post a Comment
Read more

Seismicity and Earthquakes, Isostasy and Gravity

1. Seismicity and Earthquakes in the Indian Subcontinent Key Concept: Seismicity Definition : The occurrence, frequency, and magnitude of earthquakes in a region. In India, seismicity is high due to active tectonic processes . Plate Tectonics 🌏 Indian Plate : Moves northward at about 5 cm/year. Collision with Eurasian Plate : Causes intense crustal deformation , mountain building (Himalayas), and earthquakes. This is an example of a continental-continental collision zone . Seismic Zones of India Classified into Zone II, III, IV, V (Bureau of Indian Standards, BIS). Zone V = highest hazard (e.g., Himalayas, Northeast India). Zone II = lowest hazard (e.g., parts of peninsular India). Earthquake Hazards ⚠️ Himalayas: prone to large shallow-focus earthquakes due to active thrust faulting. Northeast India: complex subduction and strike-slip faults . Examples: 1897 Shillong Earthquake (Magnitude ~8.1) 1950 Assam–Tib...
Post a Comment
Read more

Vector geoprocessing - Clipping, Erase, identify, Union & Intersection

Think of your vector data (points, lines, polygons) like shapes drawn on a transparent sheet. Geoprocessing is just cutting, joining, or comparing those shapes to get new shapes or information. 1. Clipping ✂️ Imagine you have a big map and you only want to keep a part of it (like cutting a photo into a smaller rectangle). You use another shape (like the boundary of a district) to "clip" and keep only what is inside. Result: Only the data inside the clipping shape remains. 2. Erase 🚫 Opposite of clipping. You remove (erase) the area of one shape from another shape. Example: You have a city map and want to remove all the park areas from it. 3. Identify 🔍 This checks which features from one layer fall inside (or touch) another layer. Example: Identify all the schools inside a flood zone. 4. Union 🤝 Combines two shapes together and keeps everything from both. Works like stacking two transparent sheets and redrawing t...
Post a Comment
Read more