We offer a position as an

Early Stage Researcher (M/F/D)

in the Marie Skłodowska-Curie Innovative Training Network Project MENELAOSNT in the field


Learning with adversarial samples for EO multi-spectral images (ESR14)


Adversarial samples became popular in the area of deep learning, where they have been defined as input samples subtly modified to cause a machine learning misclassification.

Generative Adversarial Networks (GAN) are a deep learning approach for generating artificial examples that plausibly could be drawn from a certain data category. GANs architecture is composed of two networks.  The first is a generator the second a discriminator. The generator is learning a generative model close to the data model, and afterward generates artificial data samples. The discriminator compare the generated and actual data by computing the probability that a sample came from the training data rather than the generated one. The objective of the discriminative network is to learn a good data representation during the adversarial process. The generative network generates artificial samples along with real samples to train the discriminative network.

In the case of EO multi-spectral images, the adversarial samples may occur “naturally”. Sensor artifacts like LSB stripes or saturation are at the origin of such samples. In addition, EO multispectral data contains physical information. Thus the adversarial information shall represent it in a consistent meaningful model. As a simple example is the transparency of a cloud to infrared radiation.

RGB bands and a non visible spectral band affected by artefacts of Sentinel 2 multispectral image
RGB and the infrared image of a cloud in a Sentinel 2 multispectral data

This project aims to provide solutions for deep learning for EO multi-spectral images in the presence of naturally occurring adversarial samples and also considering their physical nature and models.


  • The concept of adversarial samples for EO multi-spectral images.
  • Build of a database of specific adversarial samples and algorithms to generate them.
  • Study of the effects of adversarial samples for the case of DNN applied to EO multi-spectral images and design of specific DNN paradigms to alleviate the sequels of adversarial samples.


  • Image analysis, neural networks, programming languages, basic knowledge on optics could be an advantage.


  • DLR, Munich, Germany, Prof. M. Datcu, 10 months, theoretical aspects of DNN for EO multi-spectral images and related topics regarding the impact of adversarial samples.



  1. Tsagkatakis, Grigorios, et al. “Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement.” Sensors18 (2019): 3929.
  2. Qiu, Shilin, et al. “Review of artificial intelligence adversarial attack and defense technologies.” Applied Sciences5 (2019): 909.
  3. Czaja, Wojciech, et al. “Adversarial examples in remote sensing.” Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 2018.
  4. Making Machine Learning Robust Against Adversarial Inputs.

University POLITEHNICA of Bucharest is the oldest and most prestigious engineer school in Romania. Its traditions are related to the establishment, in 1818, by Gheorghe Lazăr, of the first higher technical school with teaching in Romanian, at the Saint Sava Abbey in Bucharest. In 1832, it was reorganized into the St. Sava College. On 1 October 1864, The School of Bridges and Roads, Mines and Architecture was established, which becomes, on 30 October 1867, The School of Bridges, Roads and Mines, with a duration of 5 years. Under the leadership of Gheorghe Duca, on 1 April 1881, the institution acquires a new structure, under the name of The National School of Bridges and Roads; on 10 June 1920, the Politehnica School of Bucharest was founded, with four departments: Electromechanics, Civil Engineering, Mines and Metallurgy, Industrial Section. From November 1920 the name changes to POLITEHNICA of Bucharest. On 3 August 1948, the Polytechnic Institute of Bucharest was founded, which initially included 4 faculties and in which, since 1950, have appeared most of the current faculties. Based on the resolution of the Senate of November 1992, the Polytechnic Institute of Bucharest became University POLITEHNICA of Bucharest.

The position will be located at

University Politehnica of Bucharest
Splaiul Independenţei nr. 313
060042 Bucharest, Romania

Supervisor: Prof. Dr. Daniela Coltuc

Planned Recruitment date: 1st September 2020.
Eligibility Criteria and Mobility Rule

ESRs must, at the date of recruitment, be in the first four years (full-time equivalent research experience) of their research careers and have not been awarded a doctoral degree. Full-Time Equivalent Research Experience is measured from the date when the researcher obtained the first degree entitling him/her to embark on a doctorate (either in the country in which the degree was obtained or in the country in which the researcher is recruited), even if a doctorate was never started or envisaged. Researchers can be of any nationality.

ESRs must not have resided or carried out their main activity (work, studies, etc.) in the country of the recruiting beneficiary for more than 12 months in the 3 years immediately before the planned recruitment date. Compulsory national service, short stays such as holidays, and time spent as part of a procedure for obtaining refugee status under the Geneva Convention are not taken into account.