Skip to main content

PhD, PostDoc, Group Leader and Guest Professor Positions in the area of Machine Learning and Data Analytics in Earth Observation (AI4EO).


we have several open PhD, PostDoc, Group Leader and Guest Professor Positions in the area of Machine Learning and Data Analytics in Earth Observation (AI4EO).
PhD, PostDoc, and Group Leader (Wissenschaftliche/r Mitarbeiter/in)
 

Zhu lab is a joint venue of the Professorship for Signal Processing in Earth Observation at the Technical University of Munich [www.sipeo.bgu.tum.de] and the Department EO Data Science of the Remote Sensing Technology Institute of the German Aerospace Center (DLR) [https://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-12785/22743_read-52854/]. We develop innovative signal processing and machine learning algorithms to extract geo-information from big geospatial data, ranging from remote sensing satellite data and even social media data. As downstream applications, we provide large scale and highly accurate geo-information to address societal grand challenges, such as monitoring the global urbanization, climate research and supporting the sustainable development goals of the United Nations. Our lab offers currently several open positions for outstanding PhD Candidates, postdocs, and senior scientists at either the Technical University Munich (TUM) or the German Aerospace Center (DLR). We also have open positions for outstanding research engineers.

 

Topics of particular interest to the group include:

-        Earth Observation and Computer Vision

-        Machine Learning/Deep Learning

-        Unsupervised/weakly Supervised Learning

-        Uncertainty Analysis, Interpretation and Reasoning of Deep Neural Networks 

-        AutoML

-        Anomaly and Change Detection Methods

-        Geo-information Extraction from Social Media Data

-        Natural Language Processing

-        Large-Scale Data Mining and Knowledge Discovery in Earth Observation

-        Big Data Management

-        High-performance Computing

-        Statistical Learning, Modelling, Spatial and Temporal Analysis of Geographical Observations

-        Geo-referencing, Digitalization, Building and Maintaining Large Relational Data Bases and Geo-databases, Publishing Geo-services (i.e. Web Map Services)

 

Demonstrated hands-on experience in one or more of these areas is a requirement. Postdoc applicants should have an excellent publication record and a PhD in machine learning, computer science, statistics, remote sensing, mathematics or a related discipline. Research engineer applicants should have excellent coding skills, as well as practical skills in data science and/or deep learning, and experience with scripting and running large-scale experiments.

 

Application materials comprise:

-        CV

-        Full set of transcripts

-        Statement of purpose

-        Briefly state what drives you and what are your goals in applying to the SiPEO lab

-        Names for at least 2 reference letter writers

                             For each reference, please include name, title, and email address.

                             References should expect to be contacted for a reference letter.

 

Please submit these documents to ai@DLR.de.  Please kindly consider that due to the high requests, we will not be able to consider incomplete applications.

 

AI4EO Guest Professors
 

In the framework of the BMBF funded German International AI Future Lab AI4EO, we have also a couple of slots free for guest professors with a pay scale from W1 to W3. Should you be working on any of the three topics 1) reasoning; 2) uncertainty and 3) Ethics in AI4EO and be interested in visiting us in Munich for 18 to 36 month, please kindly in direct contact with Prof. Dr. Xiaoxiang Zhu (xiaoxiang.zhu@dlr.de)

 

About Us:

 

The Technical University of Munich (TUM):

The Technical University of Munich (TUM) is one of Europe's top universities. It is committed to excellence in research and teaching, interdisciplinary education and the active promotion of promising young scientists. The university also forges strong links with companies and scientific institutions across the world. TUM was one of the first universities in Germany to be named a University of Excellence. Moreover, TUM regularly ranks among the best European universities in international rankings.

 

German Aerospace Center (DLR):

As a member of the Helmholtz Association of German Research Centers, the German Aerospace (DLR) employs more than 8000 people at 20 locations. The department "EO Data Science" at the Remote Sensing Technology Institute (IMF-DAS), located at the DLR in Oberpfaffenhofen is developing novel signal processing and AI algorithms to improve information retrieval from remote sensing data, in particular those from current and the next generation of Earth observation missions and deliver crucial geo-information to address social grant challenges, such as urbanization and climate change.

 

Resources and opportunities for collaboration:

·       Both at TUM and DLR, we are equipped with state-of-the-art computational resources such as DGX servers. In addition, we are closely collaborating with the Leibniz Supercomputing Centre (LRZ), which provides us with access to one of the most powerful supercomputing environments in Europe.

 

·       Helmholtz AI: The Helmholtz AI [link] platform aims to enhance the research within the Helmholtz Association with applied AI methods. For that each research area of Helmholtz operates HAICU units to work on short, medium and long term AI projects. IMF-DAS operates the local Helmholtz AI unit "MASTr: HAICU Munich @ Aeronautics, Space and Transport". It consists of a Young Investigator Group (YIG) in Earth observation and an AI Consulting Team, providing the expertise from Earth Observation, robotics, computer vision and an HPC/HPDA support unit. Currently we are looking for an enthusiastic Head of the YIG in the field of Large-Scale Data Mining in Earth Observation, and two PhD students.

 

·       Future AI Lab on Artificial Intelligence in Earth Observation: For the BMBF-funded Future Lab on Artificial Intelligence in Earth Observation (AI4EO), we are looking for one science manager and two PostDocs to form the backbone team of the lab starting from May 1, 2020. The Future Lab will bring together 12 highly renowned senior scientists and dozens of junior scientists from across the globe to carry out cutting-edge AI4EO research that will help to bring AI4EO to the next level. The research topics include but are not limited to reasoning, uncertainty and ethics in AI4EO.

 

 

Best regards,

Xiaoxiang Zhu

 

——————————————————

Prof. Dr.-Ing. habil. Xiaoxiang Zhu

Direcor of the German International Future AI Lab „AI4EO"

Technical University of Munich (TUM) & German Aerospace Center (DLR)

Department of Aerospace and Geodesy| Willy-Messerschmitt-Str. 1 | 82024 Taufkirchen/Ottobrunn | Germany
Website: www.ai4eo.de (up soon)

Head of Department "EO Data Science"

German Aerospace Center (DLR)

Earth Observation Center | Remote Sensing Technology Institute | Department of EO Data Science | Oberpfaffenhofen | 82234 Wessling | Germany

Telephone +49 81-5328-3531 | Telefax +49 81-5328-1420 | xiaoxiang.zhu@dlr.de

 

Professor for Signal Processing in Earth Observation

Technical University of Munich (TUM)
Signal Processing in Earth Observation| Arcisstrasse 21 | 80333 Munich | Germany

Telephone +49 89-289-22659 | Telefax +49 89-289-23202 | xiaoxiang.zhu@tum.de

 

Please visit my DLR-TUM joint research group here http://www.sipeo.bgu.tum.de/


🌍
🌏
🌎
🌐
😀
👍🏾
#Globe
#Earth
#Map
#Decision Making
#Cartography
#Cartographer
#Geography
#Geographer
#GIS
#Remote_Sensing
#RemoteSensing, #GeospatialEducation, #GeoEducation, #government, #LocalGovernment, #BigData, #geospatial, #GeoOps #ChangeDetection, #LocationIntelligence, #LocationTechnology, #SmartCities, #SmartCity, #Geomatics, #Geoinformatics, #Geology, #surveying
#StaySafe, #StayHome, #StayHealthy
#Scholarship, #Scholarships, #Onlinecourse, #Onlineducation

....


Vineesh V
Assistant Professor of Geography,
Directorate of Education,
Government of Kerala.
https://g.page/vineeshvc

Comments

Popular posts from this blog

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...

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

In the context of Remote Sensing (RS) and Digital Image Processing (DIP) , supervised classification is the process where an analyst defines "training sites" (Areas of Interest or ROIs) representing known land cover classes (e.g., Water, Forest, Urban). The computer then uses these training samples to teach an algorithm how to classify the rest of the image pixels. The algorithms used to classify these pixels are generally divided into two broad categories: Parametric and Nonparametric decision rules. Parametric Decision Rules These algorithms assume that the pixel values in the training data follow a specific statistical distribution—almost always the Gaussian (Normal) distribution (the "Bell Curve"). Key Concept: They model the data using statistical parameters: the Mean vector ( $\mu$ ) and the Covariance matrix ( $\Sigma$ ) . Analogy: Imagine trying to fit a smooth hill over your data points. If a new point lands high up on the hill, it belongs to that cl...

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...

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...