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E-2275 – RESEARCHER IN MACHINE LEARNING-URBAN AREA CLASSIFICATION USING REMOTE SENSING DATA

E-2275 – RESEARCHER IN MACHINE LEARNING-URBAN AREA CLASSIFICATION USING REMOTE SENSING DATA

Are you passionate about research? So are we! Come and join us


The Luxembourg Institute of Science and Technology (LIST) is a Research and Technology Organization (RTO) active in the fields of materials, environment and IT. By transforming scientific knowledge into technologies, smart data and tools, LIST empowers citizens in their choices, public authorities in their decisions and businesses in their strategies.


https://www.list.lu/

 

You ‘d like to contribute as a researcher? Join our Remote Sensing and Natural Resources Modelling Group!


The Environmental Research and Innovation (ERIN) department of LIST is focusing on pressing environmental challenges our society is facing today. Embedded in the department’s Environmental Sensing and Modelling (ENVISION) unit, the ‘Remote sensing and natural resources modelling’ group is carrying out impact-driven research, geared towards monitoring and predicting environmental systems in a changing world. The research group capitalizes on a blend of remote sensing data obtained from space- and air-borne platforms, as well as in-situ data measured with Internet of Things (IoT) devices, for producing information on the status of natural resources. Its research and development activities focus on the synergistic use, processing and interpretation of data from multiple complementary active and passive sensors installed on both space- and airborne platforms. The group further relies on the competences in environmental sciences such as hydrology and hydraulics, meteorology, plant physiology, geography etc. to improve the capacity in monitoring variations of Earth’s biotic and abiotic resources. Moreover, it aims to integrate remotely sensed information with in-situ data, process-based models and leverage on satellite communication, IoT and machine learning technologies in order to provide evidence-based decision support tools in near real time in a variety of thematic domains: disaster risk reduction, precision agriculture/viticulture/forestry, preservation and management of natural resources, maritime surveillance. 

Responsibilities

How will you contribute?


To strengthen its activities in natural resource modelling and in the development and application of end-to-end decision support tools, LIST is offering a temporary position for a machine leaning scientist specialized in remote sensing applications.

You will develop and evaluate Deep Learning models enabling the classification of urban areas and surface changes using satellite Earth Observation (EO) data. The research will be carried out in the framework of two projects:

-  CityWatch supported by the Luxembourg National Research Fund (FNR) through its JUMP program. You will leverage SAR and Optical remote sensing data as well as deep-learning algorithms to map buildings at different spatial resolutions, ranging from 10 m up to tenths of centimetres. Various satellite data sets will serve as input (e.g., Sentinel-1, Sentinel-2, Planet, Capella).

- CHamelEOn  aims at developing algorithms based on Artificial Intelligence to detect and interpret surface changes using  heterogeneous Earth Observation data. This activity is supported by the Directorate of Defence of the Ministry of Foreign Affairs in Luxembourg. You will develop, test and evaluate Artificial Intelligence-based change detection algorithms enabling the  synergistic processing  of heterogeneous data sets. The project further aims for a high degree of automation and generalization to support evidence-based decision making in the context of natural and man-made disaster management. You will exploit both SAR and Optical remote sensing data, as well as deep-learning algorithms to detect and interpret relevant changes at different spatial resolutions. .

You will work with an international and highly interdisciplinary team of scientists and engineers with expertise in remote sensing (optical and radar), deep-learning technologies as well as image classification.

More specifically, you will contribute:

  • Developing and coding innovative scientific Deep Learning/Machine Learning algorithms to classify urban areas and surface changes using SAR and optical data (e.g., Generative Adversarial Network (GAN), convolutional neural network aware modules, etc.)
  • Processing and analysing large collections of optical and radar satellite data.
  • Integrating and implementing scientific algorithms on high performance and distributed computing infrastructures to support the development of operational Earth Observation applications, and end-to-end decision support tools.
  • Contributing to software development, integration, testing and deployment.
  • Moreover, the appointed candidate will contribute to the dissemination, valorisation and transfer of RDI results through:
  • Software licensing.
  • Participation in the drafting of technical reports, scientific articles, patents and inventions.
  • Participation in the implementation of technological solutions (proof-of-concepts, prototypes).





 

must have requirements

Is Your profile described below? Are you our future colleague? Apply now!


Education

  • You hold a PhD in remote sensing, image or signal processing, machine learning, applied mathematics, computer engineering, telecommunications engineering or computer sciences (or similar).


Experience and skills

  • Good knowledge of EO toolkits (e.g., GDAL, SNAP, EnMAP box, etc.).
  • Excellent programming skills (e.g., Python, C/C++, Matlab, IDL, etc.).
  • Advanced knowledge of different Deep Learning and Machine Learning algorithms for supervised, unsupervised, and semi-supervised learning.
  • Experience in applying Deep Learning and Machine Learning algorithms to different data sets and in particular Earth Observation data for classification, image segmentation and geophysical parameters retrieval (e.g., Sentinel-1 and -2, Worldview, TerraSAR-X, COSMO-SkyMed, etc.).
  • Hands-on experience with at least one of the following popular Machine Learning/Deep Learning frameworks: Scikit-learn, Tensorflow, Pytorch, and Keras.
  • Knowledge of advanced statistical methods to evaluate Machine Learning models.
  • Experience with distributed cloud storage systems and cloud computing services.
  • Experience in HPC (including heterogeneous architectures).
  • Experience with image processing software.
  • Excellent communication skills in presenting scientific research and writing papers in scientific journal and technical reports.
  • Communicative and willing to learn, self-organized, and creative.
  • Ability to work both independently and collaboratively in an international team.


Language skills

  • You are fluent in English (written and oral).





nice to have requirements

Knowledge in at least one of the official languages of Luxembourg (French, German or Luxembourgish) will be considered as an asset.




We offer

Your LIST benefits


An organization with a passion for impact and strong RDI partnerships in Luxembourg and Europe that works on responsible and independent research projects;

Sustainable by design, empowering our belief that we play an essential role in paving the way to a green society;

Innovative infrastructures and exceptional labs occupying more than 5,000 square metres, including innovations such as our Viswall, high-scale incubators and top of the range 3D/4D printings that are part of our toolkit for excelling in all we do;

Multicultural and international work environment with more than 45 nationalities represented in our workforce; 

Diverse and inclusive work environment empowering our people to fulfil their personal and professional ambitions;

Gender-friendly environment with multiple actions to attract, develop and retain women in science;

32 days’ paid annual leave, 11 public holidays, flexible working hours, 13-month salary, statutory health insurance and access to lunch vouchers;

Personalized learning programme to foster our staff’s soft and technical skills;

An environment encouraging curiosity, innovation and entrepreneurship in all areas.

 

Apply online

https://www.list.lu/en/jobs/

Your application must include:

·                A motivation letter oriented towards the position and detailing your experience;

·                A scientific CV (which includes a list of the most relevant developed software, and the most relevant projects that you may have     

acquired, or that you may have contributed to)

·                List of publications (and patents, if applicable);

·                Contact details of 2 references.

 

Application procedure and conditions


  • LIST is an equal opportunity employer and is committed to hiring and retaining diverse personnel. We value all applicants and will consider all competent candidates for employment without regard to national origin, race, colour, gender, sexual orientation, gender identity, marital status, religion, age or disability;
  • Applications will be reviewed on an ongoing basis until the position is filled;
  • An assessment committee will review the applications and select candidates based on guidelines that aim to ensure equal opportunities; 
  • The main criteria for selection will be the correspondence of the existing skills and expertise of the applicant with the requirements mentioned above.


REQUIREDLANGUAGES

To be considered for this position it is crucial that you have knowledge of the following languages
  • Read B2 Upper intermediate
    Write B2 Upper intermediate
    Speak B2 Upper intermediate

OPTIONAL LANGUAGES

The following languages are optional but are considered a plus.
  • Read B2 Upper intermediate
    Write B2 Upper intermediate
    Speak B2 Upper intermediate
  • Read B2 Upper intermediate
    Write B2 Upper intermediate
    Speak B2 Upper intermediate
  • Read B2 Upper intermediate
    Write B2 Upper intermediate
    Speak B2 Upper intermediate
minimum required Education
Required work experience in years
0 or more years
Details
Employment type
Contract type
Hours per week
40
Contract period
Months
Contract duration
24
Location
Country
City
Esch-Sur-Alzette
UO
ERIN
Nature - Job type
Researcher
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