Skip to main content

PhD position in Radar remote sensing TU Wien.





PhD position in Radar remote sensing TU Wien

The research group Microwave Remote Sensing of the Department of Geodesy and Geoinformation of TU Wien is seeking a motivated

Project assistant in microwave remote sensing (f/m)

Reliable soil moisture and vegetation state estimates are an essential source of data for various research fields and applications, such as climate modelling, agricultural monitoring and flood and drought prediction. The Microwave Remote Sensing group conducts theoretical and applied research to improve the retrieval of soil moisture and land surface characteristics from active microwave remote sensing observations and use these to better understand land surface processes and interactions at different temporal and spatial scales. The Microwave Remote Sensing group is at the forefront of microwave remote sensing of land surface variables and consists of PhD's, Post-Doc's and senior scientists led by Prof. Dr. Wolfgang Wagner.

To support the research work of our team, we are looking for a Project Assistant with a strong technological interest to support our activities in the field of microwave remote sensing of soil moisture and vegetation. The selected candidate will be responsible for improving existing soil moisture and vegetation algorithms especially focusing on high resolution retrievals from Sentinel-1 backscatter observations. Working with high resolution Sentinel-1 data includes big data analysis and working in a high-performance computing environment.

Your responsibilities:

Developing scientific algorithms in the fields of radar remote sensing
Contribution in software development using object-oriented programming language
Prototyping, implementing, and testing of processing chains and generation of value-added products
Writing technical documents, project reports and scientific journal papers

Your skills

Master degree in earth sciences, environmental sciences, information sciences, geodesy, geoinformation sciences, physics, or similar
Experience in (microwave) remote sensing and derivation of geophysical parameters from remote sensing observations (e.g. soil moisture, water bodies, vegetation, snow and ice, …)
Excellent programming skills (preferably Python)
Strong analytical and technical skills and problem-solving capability
Good written and spoken communication skills in English

We Offer

The opportunity to work in an innovative, dynamic and successful team
A stimulating and friendly working environment at the department
Possibility to enrol in the PhD program of TU Wien and further develop and learn
Freedom to discuss and implement your own ideas
Flexible working hours
Workplace close to city centre, metro and main train station and ample outdoor opportunities in the vicinity of Vienna

The salary for this position is based on the Austrian regulations for university staff. The monthly minimum gross salary ranges between € 1.706,90 (MSc level) for a 25 h/week employment and € 2.731,00 for a 40h/week employment. The monthly salary is paid 14 times per year.

If this job opportunity fits your career development plans, we are looking forward to receiving your application in English (cover letter, CV, relevant publications and references) and in one single PDF file via e-mail to rs-sek@geo.tuwien.ac.at

Candidate selection will start on September 24th, 2020 and will continue until a suitable candidate is found. TU Wien will not refund any cost occurred in the course of an application.

....


Vineesh V
Assistant Professor of Geography,
Directorate of Education,
Government of Kerala.
http://geogisgeo.blogspot.com
🌏🌎
🌐🌍

Comments

Popular posts from this blog

History of GIS

1. 1832 - Early Spatial Analysis in Epidemiology:    - Charles Picquet creates a map in Paris detailing cholera deaths per 1,000 inhabitants.    - Utilizes halftone color gradients for visual representation. 2. 1854 - John Snow's Cholera Outbreak Analysis:    - Epidemiologist John Snow identifies cholera outbreak source in London using spatial analysis.    - Maps casualties' residences and nearby water sources to pinpoint the outbreak's origin. 3. Early 20th Century - Photozincography and Layered Mapping:    - Photozincography development allows maps to be split into layers for vegetation, water, etc.    - Introduction of layers, later a key feature in GIS, for separate printing plates. 4. Mid-20th Century - Computer Facilitation of Cartography:    - Waldo Tobler's 1959 publication details using computers for cartography.    - Computer hardware development, driven by nuclear weapon research, leads to broader mapping applications by early 1960s. 5. 1960 - Canada Geograph...

History of GIS

The history of Geographic Information Systems (GIS) is rooted in early efforts to understand spatial relationships and patterns, long before the advent of digital computers. While modern GIS emerged in the mid-20th century with advances in computing, its conceptual foundations lie in cartography, spatial analysis, and thematic mapping. Early Roots of Spatial Analysis (Pre-1960s) One of the earliest documented applications of spatial analysis dates back to  1832 , when  Charles Picquet , a French geographer and cartographer, produced a cholera mortality map of Paris. In his report  Rapport sur la marche et les effets du choléra dans Paris et le département de la Seine , Picquet used graduated color shading to represent cholera deaths per 1,000 inhabitants across 48 districts. This work is widely regarded as an early example of choropleth mapping and thematic cartography applied to epidemiology. A landmark moment in the history of spatial analysis occurred in  1854 , when  John Snow  inv...

Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification:   - You have prior knowledge about the c...

Pre During and Post Disaster

Disaster management is a structured approach aimed at reducing risks, responding effectively, and ensuring a swift recovery from disasters. It consists of three main phases: Pre-Disaster (Mitigation & Preparedness), During Disaster (Response), and Post-Disaster (Recovery). These phases involve various strategies, policies, and actions to protect lives, property, and the environment. Below is a breakdown of each phase with key concepts, terminologies, and examples. 1. Pre-Disaster Phase (Mitigation and Preparedness) Mitigation: This phase focuses on reducing the severity of a disaster by minimizing risks and vulnerabilities. It involves structural and non-structural measures. Hazard Identification: Recognizing potential natural and human-made hazards (e.g., earthquakes, floods, industrial accidents). Risk Assessment: Evaluating the probability and consequences of disasters using GIS, remote sensing, and historical data. Vulnerability Analysis: Identifying areas and p...

Representation of Spatial and Temporal Relationships

In GIS, spatial and temporal relationships allow the integration of location (the "where") and time (the "when") to analyze phenomena across space and time. This combination is fundamental to studying dynamic processes such as urban growth, land-use changes, or natural disasters. Key Concepts and Terminologies Geographic Coordinates : Define the position of features on Earth using latitude, longitude, or other coordinate systems. Example: A building's location can be represented as (11.6994° N, 76.0773° E). Timestamp : Represents the temporal aspect of data, such as the date or time a phenomenon was observed. Example: A landslide occurrence recorded on 30/07/2024 . Spatial and Temporal Relationships : Describes how features relate in space and time. These relationships can be: Spatial : Topological (e.g., "intersects"), directional (e.g., "north of"), or proximity-based (e.g., "near"). Temporal : Sequential (e....