News Details

Scholarships for the introduction to a research career "JAE Intro ICU 2020"
Scholarships for the introduction to a research career "JAE Intro ICU 2020"

OCTOBER 09, 2020
OCTOBER 09, 2020

 

The Artificial Intelligence Research Institute (IIIA) offers two scholarships for the introducción to a research career in the context of CSIC's JAE Intro programme.

Projects and mentors:

  1. Management of ontologies and folksonomies for defining education competencies (JAEIntro-2020-IIIA-02):
    Mentors: Carles Sierra, Marco Schorlemmer

    Education competencies are normally defined by the education departments of governments. The definition of these competencies is a process not without discrepancies and disagreements among the members of the educational community. These competencies define the educational profile of a student, who achieve them by passing certain modules and subjects. However, the set of competencies (from now on ‘tags’) is relatively general. For example, you can include the tag ‘To know how to program', but not ‘To know how to program in Python'. Also, the competency requirements of companies not only use the tags as defined by the government’s education department, but they use very different tags at times. For example, ‘To know how to manage a human team', and furthermore, this set of tags often evolves faster than the tags used in formal education. All this causes alignment problems between job applications and educational profiles. An example is the difficulty in assigning students in the “FP dual” programme as the tags of the applications and those of the students do not match well. This work would focus on defining functionalities that allow managing the ontologies that allow defining student profiles. In particular, to allow teachers and employers to add and blend new tags to an ontology (à-la-folksonomy), to define matching functions between students and job offers that take into account the relationships between tags that can be defined by all (hierarchies, meronomies, ...), to define optimal educational paths that allow to achieve the sets of competencies required by industrial training, etc. The work will be to build a webapp to be used by the education department of the government of Catalonia in the tasks associated with “FP dual” training. A pilot use of the program is planned for a small set of schools.

  2. Reinforcement learning for automating NPC behaviour in simulation environments (JAEIntro-2020-IIIA-03):
    Mentors: Jordi Sabater-Mir, Josep Lluís Arcos.
    Normally, entities not controlled by the user (NPCs) in simulation or video game environments base their behaviour on "scripts" that define how they must react to world events given their internal state. This mechanism is very laborious and requires the stage designer to foresee all the possibilities in advance. It is starting to be common the use of “reinforcement learning” (specifically “Deep reinforcement learning”) to generate behaviour in a much more automatic way and obtaining much better and more robust results. Game engines such as Unity are beginning to incorporate tools to train NPCs using "reinforcement learning" (ml-agents) and, in conferences such as SIGGraph more and more examples of the use of this technology are presented. This project will consist in the application of this technology in emergency simulation environments to automate the behaviour of NPCs that appear in a 3D scenario (whether they are civilians, mobile units or emergency personnel). Relevant knowledge: Courses in Artificial Intelligence (especially knowledge in “reinforcement learning”; advanced knowledge of Python programming; Experience with Unity.

The Artificial Intelligence Research Institute (IIIA) offers two scholarships for the introducción to a research career in the context of CSIC's JAE Intro programme.

Projects and mentors:

  1. Management of ontologies and folksonomies for defining education competencies (JAEIntro-2020-IIIA-02):
    Mentors: Carles Sierra, Marco Schorlemmer

    Education competencies are normally defined by the education departments of governments. The definition of these competencies is a process not without discrepancies and disagreements among the members of the educational community. These competencies define the educational profile of a student, who achieve them by passing certain modules and subjects. However, the set of competencies (from now on ‘tags’) is relatively general. For example, you can include the tag ‘To know how to program', but not ‘To know how to program in Python'. Also, the competency requirements of companies not only use the tags as defined by the government’s education department, but they use very different tags at times. For example, ‘To know how to manage a human team', and furthermore, this set of tags often evolves faster than the tags used in formal education. All this causes alignment problems between job applications and educational profiles. An example is the difficulty in assigning students in the “FP dual” programme as the tags of the applications and those of the students do not match well. This work would focus on defining functionalities that allow managing the ontologies that allow defining student profiles. In particular, to allow teachers and employers to add and blend new tags to an ontology (à-la-folksonomy), to define matching functions between students and job offers that take into account the relationships between tags that can be defined by all (hierarchies, meronomies, ...), to define optimal educational paths that allow to achieve the sets of competencies required by industrial training, etc. The work will be to build a webapp to be used by the education department of the government of Catalonia in the tasks associated with “FP dual” training. A pilot use of the program is planned for a small set of schools.

  2. Reinforcement learning for automating NPC behaviour in simulation environments (JAEIntro-2020-IIIA-03):
    Mentors: Jordi Sabater-Mir, Josep Lluís Arcos.
    Normally, entities not controlled by the user (NPCs) in simulation or video game environments base their behaviour on "scripts" that define how they must react to world events given their internal state. This mechanism is very laborious and requires the stage designer to foresee all the possibilities in advance. It is starting to be common the use of “reinforcement learning” (specifically “Deep reinforcement learning”) to generate behaviour in a much more automatic way and obtaining much better and more robust results. Game engines such as Unity are beginning to incorporate tools to train NPCs using "reinforcement learning" (ml-agents) and, in conferences such as SIGGraph more and more examples of the use of this technology are presented. This project will consist in the application of this technology in emergency simulation environments to automate the behaviour of NPCs that appear in a 3D scenario (whether they are civilians, mobile units or emergency personnel). Relevant knowledge: Courses in Artificial Intelligence (especially knowledge in “reinforcement learning”; advanced knowledge of Python programming; Experience with Unity.

Requirements:

  1. Bachelor s degree: At the time of application, studying or having completed a Bachelor's degree in Computer Science, Mathematics, Physics or similar disciplines in the academic year 2018-2019 or later, and not be in possession or legal disposition to obtain a PhD degree.
  2. Average grade of the undergraduate academic record: Provide proof of an average grade in undergraduate studies equal to or greater than 7.00 on a scale of 0-10 and with two decimal places, at the time of acceptance of the scholarship by the beneficiary or at the deadline for applications.
  3. Official University Master's Degree: Being enrolled, at the time of acceptance of the scholarship, in an Official University Master's Programme for the 2020-2021 academic year in the field of Computer Science and Artificial Intelligence.
  1. Bachelor s degree: At the time of application, studying or having completed a Bachelor's degree in Computer Science, Mathematics, Physics or similar disciplines in the academic year 2018-2019 or later, and not be in possession or legal disposition to obtain a PhD degree.
  2. Average grade of the undergraduate academic record: Provide proof of an average grade in undergraduate studies equal to or greater than 7.00 on a scale of 0-10 and with two decimal places, at the time of acceptance of the scholarship by the beneficiary or at the deadline for applications.
  3. Official University Master's Degree: Being enrolled, at the time of acceptance of the scholarship, in an Official University Master's Programme for the 2020-2021 academic year in the field of Computer Science and Artificial Intelligence.

Salary: 

5400€, 600€ per month
5400€, 600€ per month

Duration: 

9 months
9 months

Workday: 

20 hours per week
20 hours per week

Workplace: 

IIIA-CSIC, Campus UAB, Bellaterra (Barcelona)
IIIA-CSIC, Campus UAB, Bellaterra (Barcelona)

Start date: 

1 December 2020
1 December 2020


Applicants need to submit their applications  to the IIIA Selection Committee according to the model available at https://sede.csic.gob.es/intro2020icu), by sending an email to marco@iiia.csic.es, with the following information:

  1. Profile of the university student (name, university): Proof of identity document. In the case of non-EU foreign applicants who do not have a residence permit, their passport.
  2. Selected project. Topics other than those offered could be proposed if agreed between the candidate and the supervising researcher. Agreement that will have to be endorsed by the president of the Selection Committee.
  3. Start date of the internship and its duration. The interested student may reach an agreement with the researcher responsible for the training on the starting date and duration. Agreement that will have to be endorsed by the president of the Selection Committee.
  4. Attached documentation: CV of the applicant, letter of motivation of the applicant and academic record or certificate of the applicant. In the case of studies, partially or totally, in foreign university systems, the document generated by the Ministerio de Educación y Formación Profesional (MEFP) with the calculation of the equivalence of the qualifications obtained with the Spanish qualification scale, available to the users in the portal “Equivalencia de notas medias de estudios universitarios realizados en centros extranjeros”, together with the transcript or personal academic certificate.

Applicants need to submit their applications  to the IIIA Selection Committee according to the model available at https://sede.csic.gob.es/intro2020icu), by sending an email to marco@iiia.csic.es, with the following information:

  1. Profile of the university student (name, university): Proof of identity document. In the case of non-EU foreign applicants who do not have a residence permit, their passport.
  2. Selected project. Topics other than those offered could be proposed if agreed between the candidate and the supervising researcher. Agreement that will have to be endorsed by the president of the Selection Committee.
  3. Start date of the internship and its duration. The interested student may reach an agreement with the researcher responsible for the training on the starting date and duration. Agreement that will have to be endorsed by the president of the Selection Committee.
  4. Attached documentation: CV of the applicant, letter of motivation of the applicant and academic record or certificate of the applicant. In the case of studies, partially or totally, in foreign university systems, the document generated by the Ministerio de Educación y Formación Profesional (MEFP) with the calculation of the equivalence of the qualifications obtained with the Spanish qualification scale, available to the users in the portal “Equivalencia de notas medias de estudios universitarios realizados en centros extranjeros”, together with the transcript or personal academic certificate.


For more information, please contact Marco Schorlemmer <marco@iiia.csic.es>.

For more information, please contact Marco Schorlemmer <marco@iiia.csic.es>.