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E-2349 MACHINE LEARNING SCIENTIST SPECIALIZED IN EARTH OBSERVATION DATA APPLICATIONS

E-2349 MACHINE LEARNING SCIENTIST SPECIALIZED IN EARTH OBSERVATION DATA APPLICATIONS

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/

Responsibilities

You ‘d like to contribute as research support contributor? Join our Environmental Research and Innovation department

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 its 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.

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


How will you contribute?

  • You will develop and evaluate Deep Learning models enabling the classification of surface changes using satellite Earth Observation (EO) data. The research will be carried out in the framework of the CHamelEOn project, which 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 leverage Artificial Intelligence-based change detection algorithms enabling the synergistic processing and interpretation of heterogeneous data sets. Moreover, the project 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 relevant changes at different spatial resolutions, ranging from 10 m up to tenths of centimetres. Various satellite data sets will serve as input for this study (e.g., Sentinel-1, Sentinel-2, Planet, Capella).
  • You will work with an international and highly interdisciplinary team of scientists and engineers with expertise in remote sensing (optical and radar), deep-learning and image classification. 


More specifically, you will contribute to the CHamelEOn project by:

  • Developing and coding innovative scientific Deep Learning/Machine Learning algorithms to detect 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, you 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).

As a part of the Remote sensing and natural resources modelling team, you will participate in the CHamelEOn project which aims at developing algorithms based on Artificial Intelligence to detect surface changes based on heterogeneous Earth Observation data. This activity is supported by the Directorate of Defence of the Ministry of Foreign Affairs in Luxembourg.

Moreover, you 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!

 

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


You have a proved first experience with distributed cloud storage systems and cloud computing services, image processing software and HPC (including heterogeneous architectures).


  • 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 skills in presenting scientific research, writing papers in scientific journals, and crafting technical reports.
  • Communicative and willing to learn, self-organized, and creative.
  • Ability to work both independently and collaboratively in an international team.
  • Finally, you have a good command in English both spoken and written.


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.

 


Your application must include:

  • A motivation letter oriented towards the position and detailing your experience;
  • A scientific CV with contact details;
  • 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 C1 Advanced
    Write C1 Advanced
    Speak C1 Advanced
minimum required Education
Required work experience in years
1 or more years
Details
Employment type
Contract type
Hours per week
40
Contract period
Months
Contract duration
24
Location
Country
City
Esch-Sur-Alzette
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