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Unique identifier: 41b1e45d-775c-4b95-bfab-9e51df837b10

1. Descrição do cargo/posição/bolsa
1. Job description

Job:
international selection call for two doctorate positions under the programme SAICT-45-2017 PTDC/CCI-CIF/32607/2017, Prospecção de informação geo-referenciada de múltiplas fontes e modalidades funded by Fundação para a Ciência e a Tecnologia, in the form of employment contracts under an unspecified fixed-term work contract – in the framework of Decree-Law No. 57/2016, of August 29, regulations for hiring doctorates to stimulate scientific and technological employment in all areas of knowledge - RJEC), with the amendments introduced by Law No. 57 / 2017, dated July 19, also taking into account the provisions of Regulatory Decree No. 11-A / 2017, of December 29 and the Código do Trabalho (Labor Code), approved by Law No. 7/2009, of February 12, in its current wording - being the basis of the contracting the performance of a specific service, precisely defined and non-durable

Job/Fellowship Reference: procedimento concursal de seleção internacional para dois lugares de doutorado(a) no âmbito dos projetos aprovados no concurso SAICT-45-2017-02 PTDC/CCI-CIF/32607/2017, Prospecção de informação geo-referenciada de múltiplas fontes e modalidades financiado pela Fundação para a Ciência e a Tecnologia, em regime de contrato de trabalho a termo resolutivo incerto ao abrigo da legislação em epígrafe Código do Trabalho aprovado pela Lei n.º 7/2009 de 12 de fevereiro na sua atual redação, sendo fundamento da contratação a execução de serviço determinado, precisamente definido e não duradouro

Main research field: Engineering

Sub research field:

Job summary:

Machine learning methods are increasingly being used for the analysis of Earth observation data collected through remote-sensing, for instance in tasks such as land cover mapping. However, problems that involve combining remotely-sensed data with volunteered geographical information (e.g., ground-level photos or data from sources such as the OpenStreetMap) are only now starting to be explored, and they still involve a number of practical challenges. Over the recent years, image classification, segmentation or super-resolution, leveraging deep neural networks, have also become increasingly popular. These methods have been reported to result in impressive performance gains, when applied to problems related to processing natural images. Deep learning methods can also have several applications within GIScience research, motivating the design of tailored methods.

 

Within the MIMU project (i.e., acronym for MIning MUlti-source and MUlti-modal geo-referenced information), the researchers to be hired will work on the use of deep learning approaches for the discovery and mapping of innovative geographic knowledge, through the analysis and processing of large-scale volunteered data (e.g., geo-referenced multimedia contents) in combination with more traditional sources (e.g., remote-sensing products available in the context of initiatives like ESA's Sentinel/Copernicus programme). The complex relations between the different types of information, as well as the temporal and geographical dimensions of the data, introduce new challenges that will explored throughout the project, in an attempt to go beyond the current state-of-the-art. Research within the project will contribute to the development of spatially explicit deep learning methods, envisioning a variety of practical applications (e.g., land cover mapping, remote sensing image captioning and visual question answering, forecasting with remote sensing data, etc.).

 

One of the researchers to be hired in the project will (research position A) will develop work with a focus on image processing (e.g., considering tasks such as land cover mapping, semantic segmentation of high resolution of remote sensing imagery, or forecasting with remote sensing data), while the other (research position B) will develop work focused on jointly processing textual and visual contents (e.g., considering tasks such as remote sensing image retrieval, captioning and visual question answering, geocoding images and textual contents, etc.).



Job description:

NOTICE OF OPENING OF AN INTERNATIONAL CALL FOR THE RECRUITMENT OF A DOCTORATE UNDER DECREE-LAW No. 57/2016, OF AUGUST 29, WITH THE AMENDMENTS INTRODUCED BY LAW 57/2017, OF 19 JULY AND COMPLEMENTARY LEGISLATION

INESC-ID Public Notice number PTDC/CCI-CIF/32607/2017

1. At the meeting held at July 20, 2020 the Executive Board of INESC-ID – Instituto de Engenharia de Sistemas e Computadores INESC-ID made the a decision to open an international selection call for two doctorate positions under the programme SAICT-45-2017 PTDC/CCI-CIF/32607/2017, Prospecção de informação geo-referenciada de múltiplas fontes e modalidades funded by Fundação para a Ciência e a Tecnologia, in the form of employment contracts under an unspecified fixed-term work contract – in the framework of Decree-Law No. 57/2016, of August 29, regulations for hiring doctorates to stimulate scientific and technological employment in all areas of knowledge - RJEC), with the amendments introduced by Law No. 57 / 2017, dated July 19, also taking into account the provisions of Regulatory Decree No. 11-A / 2017, of December 29 and the Código do Trabalho  (Labor Code), approved by Law No. 7/2009, of February 12, in its current wording -  being the basis of the contracting the performance of a specific service, precisely defined and non-durable, with a view to performing the following functions:

 

Machine learning methods are increasingly being used for the analysis of Earth observation data collected through remote-sensing, for instance in tasks such as land cover mapping. However, problems that involve combining remotely-sensed data with volunteered geographical information (e.g., ground-level photos or data from sources such as the OpenStreetMap) are only now starting to be explored, and they still involve a number of practical challenges. Over the recent years, image classification, segmentation or super-resolution, leveraging deep neural networks, have also become increasingly popular. These methods have been reported to result in impressive performance gains, when applied to problems related to processing natural images. Deep learning methods can also have several applications within GIScience research, motivating the design of tailored methods.

 

Within the MIMU project (i.e., acronym for MIning MUlti-source and MUlti-modal geo-referenced information), the researchers to be hired will work on the use of deep learning approaches for the discovery and mapping of innovative geographic knowledge, through the analysis and processing of large-scale volunteered data (e.g., geo-referenced multimedia contents) in combination with more traditional sources (e.g., remote-sensing products available in the context of initiatives like ESA's Sentinel/Copernicus programme). The complex relations between the different types of information, as well as the temporal and geographical dimensions of the data, introduce new challenges that will explored throughout the project, in an attempt to go beyond the current state-of-the-art. Research within the project will contribute to the development of spatially explicit deep learning methods, envisioning a variety of practical applications (e.g., land cover mapping, remote sensing image captioning and visual question answering, forecasting with remote sensing data, etc.).

 

One of the researchers to be hired in the project will (research position A) will develop work with a focus on image processing (e.g., considering tasks such as land cover mapping, semantic segmentation of high resolution of remote sensing imagery, or forecasting with remote sensing data), while the other (research position B) will develop work focused on jointly processing textual and visual contents (e.g., considering tasks such as remote sensing image retrieval, captioning and visual question answering, geocoding images and textual contents, etc.).

 

The expiration of the contract that will operate with the communication referred to in article 345 (1) of the Código do Trabalho (Labor Code), meaning that the employer shall notify the termination of the contract to the employee, at least 7, 30 or 60 days in advance, according to whether the contract lasted up to six months, six months to two years, or per longer period.

 

2. Applicable law

Decree-Law No. 57/2016, of August 29, the regulations for hiring doctorates to stimulate scientific and technological employment in all areas of knowledge (RJEC), with the amendments introduced by Law No. 57 / 2017, dated July 19, also taking into account the provisions of Regulatory Decree No. 11-A / 2017, of December 29.

 

“Código do Trabalho” (Labor Code), approved by Law No. 7/2009, of February 12, in its current wording.

 

3. In compliance with article 13 of RJEC, the jury of the call is composed as follows:

 

President

Bruno Emanuel da Graça Martins

Assistant Professor

INESC ID - IST | UL

Member

Jacinto Paulo Simões Estima

Assistant Professor

INESC ID - IPS

Member

João Carlos Gomes Moura Pires

Assistant Professor

FCT/UNL | NOVA-LINCS

Substitute member

Alberto Manuel Rodrigues da Silva

Associated Professor

INESC ID - IST | UL

Substitute member

José Luis Brinquete Borbinha

Associated Professor

INESC ID - IST | UL

 4. The workplace shall be at INESC ID Headquarters Rua Alves Redol, 9, 1000-029 Lisboa or at any other facilities namely in INESC-ID Energia IST - Departamento de Engenharia Electrotécnica e de Computadores, Área de Energia, Av. Rovisco Pais 1049-001 Lisboa or in INESC-ID - Taguspark Avenida Professor Cavaco Silva, Edifício IST 2744-016 Porto Salvo, in accordance with the requirements and needs of research projects.

 

5. In compliance with Portaria nº 1553-c/2008 of 31 of December 2008 the monthly remuneration corresponds to level 33 of the single remuneration table (TRU), which corresponds to a monthly remuneration of 2.2134.73 Euros.

 

6. Any national, foreign and stateless candidate(s) holding a doctorate degree in computer engineering, geomatic engineering or other related scientific area and a suitable scientific and professional curriculum may submit an application. In case of doctorate degrees awarded by a foreign higher education institution, the degree must comply with the provisions of Decree-Law no. 341/2007 of 12th of October, and all formalities established therein must be fulfilled, by the selected candidate, until the date of signature of the contract.

 

7. General call admission requirements include those specified in the previous point and that, from the candidate’s curriculum vitae, one may validate the compliance with productivity criteria such as:

• Number of articles published in Q1 or Q2 journals (Scimago or Web of Science) related to areas such as machine learning, data mining, remote sensing, and/or geographic information systems (at least 2 articles);

• Participation in previous research projects related to areas such as machine learning, data mining, remote sensing, and / or geographic information systems (at least 1 previous project);

8. The evaluation of the scientific and curricular path focuses on relevance, quality and timeliness in the scientific area of the call:

a) of the scientific, technological, academic, cultural or artistic production during the last five years and considered more relevant by the candidate; [35 %]

b) of the applied or practice-based research activities developed over the last five years and considered as having the greatest impact by the candidate; [30 %]

c) of the activities of extension and dissemination of knowledge developed during the last five years, in particular in the context of promoting the culture and scientific practices considered by the candidate to be of greater relevance; [20 %]

d) of the activities of science, technology and innovation programs management, in Portugal or abroad. [15 %]

 

9. The period of five years referred to in the previous point may be increased by the jury, at the request of the candidate, when justified on grounds of suspension of scientific activity for socially protected reasons, namely for reasons of parental leave, prolonged serious illness, and other situations of unavailability for work legally protected.

 

10. The evaluation of the scientific and curricular career has two components, namely

i) The assessment of the curriculum vitae and other documentation presented is made in according to point 7 and 8 of this notice. This component of the evaluation is expressed in a scale of 0 to 100

 

ii) The Juri may interview the first three candidates with higher classification in person or in the impossibility of attending by video conference. The evaluation of the interview is expressed in a scale of 0 to 100 taking into consideration the quality of the scientific knowledge and the capacity of communication demonstrated by the candidate, as well as the answers to the questions placed by the members of the evaluation panel.

 

If an interview is conducted, the punctuation proposed by each member of the jury is obtained by assigning a weighting factor of 90% to the curriculum vitae and other documentation presented, and a weighting factor of 10% to the interview

 

11. Each member of the jury assigns a classification to each of the candidates on a scale of 0 to 100 points, ranking the candidates according to their classification consisting on the sum of the partial classifications assigned in each evaluation criterion, and taking into account the weighting factor given to each parameter. In this process abstentions are not allowed

 

12. Candidates shall be ordered by applying the successive voting method

 

13. The jury has the faculty not to select a candidate in case the requirements mentioned in points 6 and 7 are not fulfilled.

 

14. Minutes of the jury meeting are drawn up, which contain a summary of what has taken place in the meeting, as well as the votes cast by each of the members and their reasons, being available to the candidates whenever requested.

 

15. The final deliberation of the jury is approved by the President of INESC-ID, and it is his responsibility to establish the respective contract.

 

16. Formalisation of applications

16.1 Applications are made by sending an email to rh@inesc-id.pt, with the documents stated in 16.2 and 16.3.

 

16.2 Applications are formalised by sending a Motivation Letter, addressed to the Board of INESC ID, including this announcement identification, full name, parents’ names, ID card / passport number and expiration date, taxpayer ID number, date and place of birth, marital status, occupation, residence and contact address, including email address and telephone

 

16.3 Applications shall include all supported documents encompassed by point 6 and 7 for call admission, namely:

a) Copy of certificate or diploma;

b) Detailed curriculum vitae, structured in accordance with the items in point 8

 

* The administrative verification of the recognition of doctoral degrees obtained abroad will be fulfilled for the purpose of contracting, in case of approval of the candidatures.

 

17. This call is open from August 3 to 31, 2020.

 

18. Candidates who formalize their application incorrectly or who do not prove the requirements required in this notice are excluded from admission. In case of doubt, the jury may demand any candidate to present documents proving those statements.

 

19. The false statements made by the candidates will be punished according to the law

 

20. The admitted and excluded candidates applicants will be notified by email of the final ranking list

 

21. Prior Hearing and Deadline for Final Decision: After being notified, candidates have 10 working days to submit a formal rebuttal. Within the term of 30 days, counted from the deadline for the presentation of the candidacies, the final decisions of the jury are given.

 

22. This call is exclusively intended to fill the vacancy (s) indicated and may be terminated until the homologation of the final ranking list of candidates and expiring with the respective occupation of the working position on offer.

 

23. Non-discrimination and equal access policy: INESC-ID actively promotes a policy of non-discrimination and equal access, so that no candidate can be privileged, benefited, disadvantaged or deprived of any right or exemption from any duty owing, in particular, to ancestry, age, sex, sexual orientation, marital status, family status, economic situation, education, social origin or condition, genetic heritage, reduced working capacity, disability , chronic illness, nationality, ethnic origin or race, territory of origin, language, religion, political or ideological beliefs and trade union membership.

 

24. Under the terms of D.L. No. 29/2001, of February 3, a disabled candidate has a preference when in equal classification, which prevails over any other legal preference. Candidates must declare on the application form their respective degree of disability, the type of disability and the means of communication / expression to be used in the selection process, under the terms of the aforementioned diploma.

 

25. The selection panel approved this announcement on. July 14, 2020

 

 




Vacant posts: 2

Type of contract: Information not available

Job country: Portugal

Job city: Lisboa

Job company/institute: inesc id


Application deadline: 31 Agosto 2020
(The Application's deadline must be confirmed on the Job Description)

2. Dados de contactos da organização
2. Organization contact data

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3. Habilitações académicas
3. Required education Level


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4. Línguas exigidas
4. Required languages


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5. Experiência exigida em investigação
5. Required research experience


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