• Must not have resided or carried out their main activity (work, studies, etc.) in the country of the host institution for more than 12 months in the 3 years immediately prior to the recruitment date.
• Must hold a Master’s degree in a relevant field (e.g., Artificial Intelligence, Computer Science, Data Science, Mathematics, Physics, Engineering, or related disciplines) by the start date of the position. Applicants who have already completed a PhD are not eligible.
• Demonstrated deep scientific and technical knowledge, evidenced by publications or successful projects, in at least one of the following areas:
  • Machine learning and neural network architectures (e.g., convolutional, recurrent, and transformer networks)
  • Generative AI
  • Federated Learning
  • Graph Neural Network
  • Large Language Models
  • Scientific Machine Learning
  • Big data technologies and tools, with the capability to work with large datasets to derive
  • Predictive analytics and insights
  • Familiarity with the latest AI trends and developments, and the ability to apply these
  • Advances to practical, real-world challenges
• Proficiency in Python is mandatory. Knowledge of additional programming languages such as C++, R, or Rust is considered a plus.
• Proficiency in using key Machine Learning and Deep Learning frameworks, particularly TensorFlow and PyTorch, is mandatory. Proficiency with complementary libraries like Keras, Lightning, TensorBoard or WandB is also a plus.
• Familiarity with Visual Studio and its integration with relevant programming languages.
• Strong knowledge of Linux systems.
• Experience using version control systems such as Git, GitHub, GitLab, or similar tools for collaborative software development and version management.
• An excellent academic record and proficiency in English (both written and spoken) at a minimum B2 level according to the European Framework of Reference. Proficiency will be assessed during the interview, and proof of language competency (e.g., certificate) may be required.

Interested candidates should submit the following documents to <david.camacho@upm.es>

with the email subject: "TUAI - Name Surname" (e.g., "TUAI - John Doe"):

1. A detailed and updated scientific CV, including your contact information, and highlighting educational qualifications (e.g., Laurea degrees), relevant professional experience (e.g., internships), programming languages known, and tools or technologies used. Please highlight starting availability. The CV should be saved as name_surname.pdf (e.g., john_doe.pdf). List of publications and patents (if applicable).
2. A motivation letter (maximum 2 pages) highlighting your experience and alignment with the ESR position. Alternatively, you may provide a video motivation (maximum 2 minutes). While the video format is preferred, it is not mandatory. However, the written motivation letter is compulsory. If submitting a video, please include a link to the video rather than attaching it directly to the email.
3. Name and contact details of at least one recommendation letter of support for your application or being contacted. 
4. Copies of your degree certificates and academic transcripts. In case, MSc is to be completed before the starting of this position, please provide expected final grade and completion date and availability. 
5. Any English language certificate and other relevant certificates (if applicable).
6. MANDATORY APPLICATION FORM: Complete the application form available at https://forms.gle/FK8XHUxmqUWvCSrw7. Submission of this form is compulsory. Applicants who complete steps 1-6 but do not fill out the form will not be considered for the position. Please ensure that all information provided in the form is truthful, as it will be thoroughly reviewed and verified during the interview process!

Selection Process

The selection process will consist of the following stages:

1. Initial pre-screening: Candidates whose profile is deemed suitable based on their CV, motivation letter, and references will move on to the second stage, the Technical Challenge.
2. Technical challenge: Candidates in this stage will be provided with a challenge description and goals. The candidate will present a solution to the challenge with a solution with a grounded state-of-the-art understanding of the core issues presented in said challenge. The candidates will be evaluated on the quality of their technical solution. The challenge proposed will evaluate the knowledge of the candidate in the field of time series and data visualization, having to demonstrate an acceptable degree of expertise in both. The candidate will need to submit: 1) a complete and detailed report, preferably following a scientific communication format, detailing the implemented solution and obtained results; 2) a git repository with the executable source code.
3. Presentation of results: If the quality of the presented results is sufficient, the candidate will be invited to present the implemented solution and obtained results to a panel of experts and scientists from the TUAI consortium. The presentation will have a maximum duration of 30 minutes, followed by a question-and-answer session. The candidate must provide a presentation beforehand, along with any other materials they consider useful for evaluating their solution.
4. Final Interview: The selected candidates will be called to a final meeting with members of the Polytechnic University of Madrid, where various aspects related to their research interests and communication skills will be evaluated. Their CV will be reviewed and assessed, and they will need to respond to any technical, research, or research-career-related questions from the panel. The candidate may also ask any questions they consider appropriate (for example, regarding the development of their professional or research career) to the panel members.
5. Final Decision Communication: Lastly, candidates who participated in the results presentation and the final interview will be officially informed by the project’s Principal Investigator (PI, Prof. David Camacho) of the decision made by the various evaluation panels regarding their application.

Remuneration

The successful candidate will receive an attractive salary: 32K € annual gross salary without family (living + mobility allowances); 38K € annual gross salary with family (living + mobility + family allowances), in accordance with the MSCA regulations for Early-Stage Researchers. The exact (net) salary will be confirmed upon appointment and is dependent on local tax regulations and on the country correction factor (to allow for the difference in cost of living in different EU Member States). In addition to the base salary, there are additional allowances provided, such as a living allowance, a mobility allowance and a family allowance (if applicable), which further increase the total compensation package. 

Furthermore, TUAI will offer to take advantage of joint scientific research training, transferable skills workshops, and international conferences. For more information about the project and consortium contact us (email below).

Additional Benefits

- International Research Environment: Join one of the most relevant and active research groups in the field of artificial intelligence in Spain (AI+DA lab: https://aida.etsisi.upm.es), an international research hub that fosters collaboration across academic and industry sectors, providing an enriching environment for professional and personal growth.

- Mobility and Collaboration: Experience mobility across prestigious partner institutions and industry partners in multiple countries, gaining exposure to diverse research methodologies and collaborative projects.

- Living in Madrid: Immerse yourself in the captivating city of Madrid, celebrated for its storied history, diverse cultural scene, exquisite gastronomy, and dynamic lifestyle, all with a relatively accessible cost of living. As Spain’s vibrant capital, Madrid offers easy access to historic neighborhoods, beautiful parks, renowned art institutions like the Prado and Reina Sofía museums, as well as a lively nightlife and culinary landscape that spans traditional tapas to innovative cuisine. Madrid’s central location also makes it convenient for exploring Spain, with quick connections to Toledo, Segovia, and Salamanca. https://www.esmadrid.com/en.

● Must not have resided or carried out their main activity (work, studies, etc.) in the country of the host institution for more than 12 months in the 3 years immediately prior to the recruitment date.
● Must hold a Master’s degree in a relevant field (e.g., Artificial Intelligence, Computer Science, Data Science, Mathematics, Physics, Engineering, or related disciplines) by the start date of the position.
Applicants who have already completed a PhD are not eligible.
● Demonstrated deep scientific and technical knowledge, evidenced by publications or successful projects, in at least one of the following areas:
○ Machine learning and neural network architectures (e.g., convolutional, recurrent, and
transformer networks)
○ Generative AI
○ Federated Learning
○ Graph Neural Network
○ Large Language Models
○ Scientific Machine Learning
○ Big data technologies and tools, with the capability to work with large datasets to derive
○ Predictive analytics and insights
○ Familiarity with the latest AI trends and developments, and the ability to apply these
○ Advances to practical, real-world challenges
● Proficiency in Python is mandatory. Knowledge of additional programming languages such as C++, R, or Rust is considered a plus.
● Proficiency in using key Machine Learning and Deep Learning frameworks, particularly
TensorFlow and PyTorch, is mandatory. Proficiency with complementary libraries like Keras, Lightning, TensorBoard or WandB is also a plus.
● Familiarity with Visual Studio and its integration with relevant programming languages.
● Strong knowledge of Linux systems.
● Experience using version control systems such as Git, GitHub, GitLab, or similar tools for collaborative software development and version management.
● An excellent academic record and proficiency in English (both written and spoken) at a minimum B2 level according to the European Framework of Reference. Proficiency will be assessed during the interview, and proof of language competency (e.g., certificate) may be required.

Interested candidates should submit the following documents to <david.camacho@upm.es>

with the email subject: "TUAI - Name Surname" (e.g., "TUAI - John Doe"):

1. A detailed and updated scientific CV, including your contact information, and highlighting educational qualifications (e.g., Laurea degrees), relevant professional experience (e.g., internships), programming languages known, and tools or technologies used. Please highlight starting availability. The CV should be saved as name_surname.pdf (e.g., john_doe.pdf). List of publications and patents (if applicable).
2. A motivation letter (maximum 2 pages) highlighting your experience and alignment with the ESR position. Alternatively, you may provide a video motivation (maximum 2 minutes). While the video format is preferred, it is not mandatory. However, the written motivation letter is compulsory. If submitting a video, please include a link to the video rather than attaching it directly to the email.
3. Name and contact details of at least one recommendation letter of support for your application or being contacted. 
4. Copies of your degree certificates and academic transcripts. In case, MSc is to be completed before the starting of this position, please provide expected final grade and completion date and availability. 
5. Any English language certificate and other relevant certificates (if applicable).
6. MANDATORY APPLICATION FORM: Complete the application form available at https://forms.gle/FK8XHUxmqUWvCSrw7. Submission of this form is compulsory. Applicants who complete steps 1-6 but do not fill out the form will not be considered for the position. Please ensure that all information provided in the form is truthful, as it will be thoroughly reviewed and verified during the interview process!

Selection Process

The selection process will consist of the following stages:

1. Initial pre-screening: Candidates whose profile is deemed suitable based on their CV, motivation letter, and references will move on to the second stage, the Technical Challenge.
2. Technical challenge: Candidates in this stage will be provided with a challenge description and goals. The candidate will present a solution to the challenge with a solution with a grounded state-of-the-art understanding of the core issues presented in said challenge. The candidates will be evaluated on the quality of their technical solution. The challenge proposed will evaluate the knowledge of the candidate in the field of time series and data visualization, having to demonstrate an acceptable degree of expertise in both. The candidate will need to submit: 1) a complete and detailed report, preferably following a scientific communication format, detailing the implemented solution and obtained results; 2) a git repository with the executable source code.
3. Presentation of results: If the quality of the presented results is sufficient, the candidate will be invited to present the implemented solution and obtained results to a panel of experts and scientists from the TUAI consortium. The presentation will have a maximum duration of 30 minutes, followed by a question-and-answer session. The candidate must provide a presentation beforehand, along with any other materials they consider useful for evaluating their solution.
4. Final Interview: The selected candidates will be called to a final meeting with members of the Polytechnic University of Madrid, where various aspects related to their research interests and communication skills will be evaluated. Their CV will be reviewed and assessed, and they will need to respond to any technical, research, or research-career-related questions from the panel. The candidate may also ask any questions they consider appropriate (for example, regarding the development of their professional or research career) to the panel members.
5. Final Decision Communication: Lastly, candidates who participated in the results presentation and the final interview will be officially informed by the project’s Principal Investigator (PI, Prof. David Camacho) of the decision made by the various evaluation panels regarding their application.

Application Conditions and Equal Opportunity

We kindly ask applicants to provide their nationality and gender for statistical purposes only, as part of our commitment to promoting diversity and ensuring equal opportunities within our workforce. This information will be kept strictly confidential and will not be used in any discriminatory manner. All applications will be considered impartially, without discrimination based on nationality, race, color, gender, sexual orientation, gender identity, marital status, religion, age, or disability.

Applications will be reviewed on an ongoing basis until the position is filled. The selection process will be carried out by an assessment committee that follows guidelines designed to ensure equal opportunity for all candidates. The primary criteria for selection will be the alignment of the applicant’s qualifications and expertise with the specified requirements. Female candidates are particularly encouraged to apply, as gender balance will be taken into consideration during the evaluation process to support women’s representation in science and research fields.