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Advanced School in AI

 Overview

This is a postgraduate Advanced School (Italian: “Scuola Avanzata, post laurea specialistica”) designed to meet the fast-growing demand of the job market  for new skills and competencies related to advanced fields of Artificial Intelligence (AI) and relevant for the economy, research system, administration, and society.

The School aims to form a new generation of highly specialised professionals and researchers in the innovative fields of AI and Machine Learning (in particular deep neural networks), and of computational modelling of brain, behaviour and society  (Computational Embodied Neuroscience, Computational Social Science). These topics do not cover the several topics encompassed by Artificial Intelligence, but are a starting point building on the core competences of the research network currently supporting the School, but the network is open to expansion and in the future the School will include other areas of AI.

The School also aims to provide upskilling in the new AI fields, for both professionals and researchers. 

The key distinguishing element of the School is the aim to lead the students to a deep understanding of the learned computational approaches and techniques. This will be based on:

  • A close integration between theoretical learning and applications, pivoting on a real-life student-centered project involving innovative Companies and outstanding Research Centres
  • A supporting environment involving a close interaction between the academia and companies
  • A highly motivating interdisciplinary environment 

 

Fundamental structure of the School

  • 4 months of courses (8 hours x 2 days per week)
  • 4 to 8 months of internship within Company or Research Group
  • Project in coordination with Company or Research Group since the first day of the School

School main advantages for the Sponsoring Companies and for the Supporting Research Institutes

  • Selection and formation of talented students: thanks to its hub-role within a rich network of academic and research institutes, the School will can attract and select highly talented students and form them in the advanced fields of the School. These students represent a highly valuable asset from which the Sponsoring Companies and the Supporting Research Institutes could draw talented personnel for their development in advanced fields of AI.
  • Project decided together with the Sponsoring Company/Supporting Research Institute: since the first day of the school, the student will work on a project previously identified by the School together with the Company or Research Group and relevant for the latter. This will bring utility for both the Company/Research Group, as the project will address a key problem relevant form them, and for the student, as she/he will work on a concrete, important real-life project.
  • Networking and acquisition of know-how on breakthrough new AI technologies: possibility of entering a network involving an interdisciplinary set of advance-research and economic agents, and thus of accessing competitive advantages rendered by new AI technologies.

Three Learning Programs for three typologies of students

  • Upskilling Learning Program, for already Employed Professionals and Researchers. This program (4 months) has been designed for an upskilling of already employed professionals and researchers seeking to enter, or be updated on, the advanced topics of AI treated by the School.
  • Postgraduate students (Italian: students with a “laurea specialistica”); or university students who have finished, or close to finish, their exams within a university master (Italian: students seeking a “laurea specialistica”) and wishing to carry out their research thesis within the School. These students could be interested in either one of the following Program:
    • Company Internship Program, for students interested in company applications and seeking a professional career: these  students will be involved in a specific application project identified and supervised by a Company Tutor and by an expert School Mentor in one of the advanced fields of the School. The internship will be carried out within the company. This Program will open up for the student important possibilities for professional employment after the School completion.
    • Research Internship Program, for students interested in the investigation of reality through AI tools, or to develop AI techniques, and seeking a research career: these students will be involved in a specific research project proposed and supervised by a  Research Tutor from the Research Group hosting the internship, in one of the advanced research fields of the School. This activity will firmly aim to arrive to a scientific publication, the main means to enroll in a funded PhD program in Italy or abroad, and start a research career.

School main features for the students

  • Student’s profound theoretical understanding, and capacity to apply to real-life problems:
    • Machine learning and deep neural networks techniques, and/or
    • Computational models for understanding reality, in particular brain, mind, and society
  • Project since the first day: based on a real-life company/research challenging problem, involving interdisciplinary teams of senior professionals and researchers
  • School program tailored on the student: the learning and training path followed by each student will be highly personalised based on her/his project, learning needs, and interests, drawing from the multiple School resources  (Fundamentals Courses, Specialistic Courses, Focused Courses; Interdisciplinary student activities; School Mentors/Tutors; School Advisors).
  • Acquisition of key soft skills: the School will aim to give to the student not only knowldge and content-related skills, but also soft skills related to project working, flexibility, continuous-learning, problem solving, professionalism, SMART-goal setting, efficient time management, self-coaching, team work.
  • Networking, possibility of starting a high-profile career in AI fields: the close interaction of the students with important high-tech Companies and Research Centers will allow them to start a high-profile career as professionals or as PhD Students/Researchers.

Three Tracks of specialisation

The School learning environment is highly interdisciplinary having a focus on both artificial and natural intelligent systems. The student chooses one of 3 Tracks for his/her application/scientific specialisation:

  • Track 1: Machine Learning Applications (MLA):
    AI and Machine Learning techniques relevant for applications, with a particular focus on deep neural networks. This Track is especially oriented to applications relevant for companies, but also hosts students interested in making basic research to develop new algorithms and intelligent systems for AI/Machine learning/Robotics.
  • Track 2: Computational Embodied Neuroscience (CEN):
    AI and Computational Modelling techniques relevant for investigating the brain, mind and behaviour. This Track is especially oriented to research, but it also hosts students interested in the development of applications involving AI/Machine learning/Robotics tools and closely related to brain/mind/society.
  • Track 3: Computational Social Science (CSS):
    AI and Computational Modelling techniques relevant for investigating people’s behaviour and social phenomena. This Track is especially oriented to research, but it also hosts students interested in the development of applications involving AI/Machine learning/Robotics tools and closely related to behaviour/society.

Three Learning Programs

The School offers 3 different Learning Programs among which the student can choose to follow the selected Track:

  • Upskilling Program – AI for Industry 4.0 or for Research:
    • Total of 4 months, 2 days per week (8 hours each), about 128 hours off-classroom of Mini Project, about 288 hours of classroom Courses organised as follows:
      • 2 months of Fundamental Courses (common to all Tracks)
      • 2 months of Specialisation Courses (specific to Tracks)
      • 4 months: Focused Courses (variably selected)
  • Company Internship Program – Build real-world machine learning solutions:
    • Total of 4 months, 2 days per week, about 128 hours of off-classroom Project start, about 288 hours on classroom Courses organised as follows:
      • 2 months of Fundamental Courses (common to all Tracks)
      • 2 months of Specialisation Courses (specific to Tracks)
      • 4 months: Focused Courses (variably selected)
    • Internship in Company with duration (between 4 and 8 months) and salary negotiated with company
    • Possible final hiring
  • Research Internship Program – Start a career in research:
    • 4 months, 5 days per week,about 288 h on Courses, 
    • Total of 4 months, 2 days per week, about 128 hours of off-classroom Project start, about 288 hours on classroom Courses organised as follows:
      • 2 months of Fundamental Courses (common to all Tracks)
      • 2 months of Specialisation Courses (specific to Tracks)
      • 4 months: Focused Courses (variably selected)
    • Internship with Research Group with duration (between 4 and 8 months) and bursary negotiated with Group
    • Possible final publication

Selection criteria and procedure

  • BA/MA (Italy: “laurea specialistica”); alternatively, termination (or close to termination) of all university exams, with thesis focused on the mini-project/project of the School
  • Curriculum
  • Motivation letter
  • Draft proposal on a project selected among the proposed ones
  • Interview

Maximum Available Places: 35
Detailed application instructions are available here.

Prerequisites of perspective students, and knowledge acquired with the School

The school is open to students of all university faculties/departments. The philosophy of the School is indeed that variety is an important asset to foster the School interdisciplinary nature and is in line with its long-life learning open perspective. In this respect the School should be intended as a container of resources from which specific resources will be drawn to tailor a specific learning path onto the learning needs and project of the specific student.

For this reason, it is very important that if you are a perspective student you contact us as soon as possible, before the submission, to evaluate together your case and consider possibilities that fit your needs and goals!

Instead, important prerequisites to fully benefit of the School program are a keen motivation, interest on AI, will to learn, desire to interact with and act within an interdisciplinary network.

We have identified three groups of students coming from different university backgrounds and hence having different knowledge and learning needs as follows.

Students from biology/neuroscience, psychology, philosophy, law, economics, political sciences, communication sciences:

  • Knowledge and skills already possessed:
    (1) Extensive knowledge of the contents of a specific topic
  • Knowledge and skills to acquire from the School:
    (2) Capacity to face problems with a quantitative approach and mindset, acquisition of fundamental math and statistics notions needed for machine learning;
    (3) Capacity to program; knowledge and skills on AI approaches and techniques
    (4) Capacity to apply the acquired knowledge and skills to real-life research/application problems

Students from math, physics, chemistry, statistics:

  • Knowledge and skills already possessed:
    (1) Extensive knowledge of the contents of a specific topic;
    (2) Capacity to face problems with a quantitative approach and mindset
  • Knowledge and skills to acquire from the School:
    (3) Capacity to program; complements of math and statistics oriented to machine learning; in-depth understanding and skilled-use of AI approaches and techniques
    (4) Capacity to apply the acquired knowledge and skills to real-life research/application problems

Students from engineering, and computer science:

  • Knowledge and skills already possessed:
    (1) Extensive knowledge of the contents of a specific topic;
    (2) Capacity to face problems with a quantitative approach and mindset
    (3) Capacity to program; possibly some knowledge on AI
  • Knowledge and skills to acquire from the School:
    (3) Complements of math and statistics oriented to machine learning; in-depth understanding and skilled-use of AI approaches and techniques
    (4) Capacity to apply the acquired knowledge and skills to a real-life research/application problems

Additional advantages for students

Students interested in a professional career (in companies or start-ups):

  • Internship involving a project on a company need, carried out within the company with the assistance of the School experts
  • Getting contacts and becoming part a network formed by top-level/advanced-technology research institutes, companies, researchers, and professionals: a fundamental element to build a future professional career in the booming sector of big-data analysis and AI
  • Possibility of final hiring in a company, or to create own start-ups (contacts with start-up incubators/accelerators)

Students interested in a career as researchers:

  • Internship involving a project on state-of-the-art research problem within a top-level research group
  • Getting contacts and becoming part a network formed by top-level/advanced-technology research institutes, companies, researchers, and professionals: a fundamental element to find a PhD in a topic of preference and make the first step to become a researcher
  • Scientific publication(s), the key element to win a national or international PhD enrollment with bursary

Important dates

  • 19 July 2018: Third Open Day
  • 13 September 2018: Last Open Day
  • 26 September 2018: Deadline for submission of student applications (contact the School administration for applications beyond this deadline)
  • 27 September – 3 October 2018: Student’s interviews and Selection 1
  • 4 October 2018:
    • Communication of outcome of students’ Selection 1
    • Start of students enrollment
  • 18 October 2018: School start
  • 8 March 2019:
    • End of Courses
    • Diploma for Upskilling Program students
    • Selection 2: Examination for passing to Internship phase
    • Start of Internship for Company and Research Internship Program students
  • 8 July-October 2019: End of Internship and Diploma for Company and Research Internship Program students

Fee, scholarships, internship

  • Fee without scholarship: 4000 €
  • Most students will be assigned a scholarship. The scholarship amount will be assigned to each student based on his/her learning needs, curriculum, background, etc. (the scholarship amount will be communicated with the outcome of Selection 1, i.e. the School admission).
    Depending on the type of scholarship, this will be transferred directly to the student, who will use it to cover the whole fee, or to the School, allowing a lower fee.
    The minimum cost for the student with highest scholarship: 500 €
  • Average fee expected for each student: 1500 €
  • Internship salary for students:
    • Internship salary and duration will be negotiated with the company/research lab. Expected salary is in the order of 500 /month for 5/12 months
    • Some internships at research labs fund living expenses for 300 /month

Final School diploma 

The final “Advanced School in AI Diploma” will specify the successful attendance of the School in the successfully accomplished Track:

  • Advanced School in AI: Machine Learning Applications
  • Advanced School in AI: Computational Embodied Neuroscience
  • Advanced School in AI: Computational Social Science

Moreover, the Diploma will specify the Courses (names, duration, student proficiency), Project title, and Internship (when present).