Overview

This is a postgraduate Advanced School (Italian: “Scuola Avanzata, post laurea specialistica”) aimed at answering 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 is highly interdisciplinary and open to all university backgrounds, with an innovative educational methodology designed for enabling all students,  even with no previous programming skills, to a deep understanding and use of AI new approaches and techniques. 

The key distinguishing elements of the School are:  

• a highly motivating interdisciplinary environment, having a broad perspective on both artificial and natural intelligent systems;
• a personalized training path, with the possibility to build an individual study plan on more than 30 available Courses, centered  on  the individual project work, learning needs, and interests;
• a real-life student-centered project involving innovative Companies and outstanding Research Centres; 
• a supporting growing network involving a close interaction among Research, Academia and Companies. 

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. 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. 

Fundamental structure of the School

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

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

• Selection and formation of talented students
• Project decided together with the Sponsoring Company/Supporting Research Institute
• Networking and acquisition of know-how on breakthrough new AI technologies

Three Learning Programs for Upskilling and Training

• Upskilling Learning Program, for already Employed Professionals and Researchers. 
• 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
• 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.

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). Alternatively, the student can select one of the three learning and training paths (Tracks) proposed by the School.

The School learning environment is highly interdisciplinary having a focus on both artificial and natural intelligent systems. The student could, for example, chooses a learning and training path oriented to one of the three track below: 

• 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 personalized learning and training path: 

• Upskilling Program – AI for Industry 4.0 or for Research:
  • Total of 5 months, 352 hours of lessons and classroom activities, for 2 days per week (8 hours each),  50 hours off-classroom of Basic Project, for a total of 400 hours organised as follows:
    • 2 months of Fundamental Courses
    • 3 months of Specialisation Courses of Machine Learning Applications, Computational Embodied Neuroscience, Computational Social Science
  • Diploma after Courses
• Company Internship Program – Build real-world machine learning solutions:
  • Total of 5 months, 352 hours of lessons and classroom activities,for 2 days per week (8 hours each),  50 hours off-classroom of Basic Project and 100 hours off-classroom of Company Project, for a total of 500 hours organised as follows:
    • 2 months of Fundamental Courses
    • 3 months of Specialisation Courses of Machine Learning Applications, Computational Embodied Neuroscience, Computational Social Science
  • Internship in Company with duration (between 4 and 7 months) and salary negotiated with company
  • Possible final hiring
  • Diploma after internship
• Research Internship Program – Start a career in research:
  • Total of 5 months, 352 hours of lessons and classroom activities, for 2 days per week (8 hours each),  50 hours off-classroom of Basic Project and 100 hours off-classroom of Research Project, for a total of 500 hours organised as follows:
    • 2 months of Fundamental Courses
    • 3 months of Specialisation Courses of Machine Learning Applications, Computational Embodied Neuroscience, Computational Social Science
  • Internship with Research Group with duration (between 4 and 7 months) and possible salary negotiated with Group
  • Possible final publication
  • Diploma after internship

It is mandatory the attendance of at least 200 hours of classroom Courses.

Selection criteria and procedure

• BA/MA (Italy: “laurea specialistica”) or BA (Italy: “laurea triennale”); alternatively, termination (or close to termination) of all university exams
• Curriculum
• Motivation letter
• Draft proposal on a possible AI Project that the student would develop during the School
• Interview

Maximum Available Places: 50 in classroom. Courses attendance is mandatory. Courses will be available in live streaming or offline. 

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

• 30 September 2021: Deadline for submission of student applications (contact the School administration for applications beyond this deadline)
• By 1 October 2021:
  • Communication of outcome of students’ Selection 1
  • Start of students enrollment
• Before 7 October 2021: Payment of first part of the fee (1000 €).
• 7 October 2021: School start
• Before 15 January 2022: Payment of remaining part of the fee
• 25 March 2022:
  • 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
• April-October 2022: End of Internship and Diploma for Company and Research Internship Program students

Fee, scholarships, internship

• Student fee: 2200 €
• Costs for companies:  starting from 5000 € (contact the School)
• Scholarship for students available: see scholarship page.
• The School enrollment will give the right, and require, to become members (“socio”) for free of the Association “Associazione culturale science2mind” that is managing the School organisation.

Final School diploma 

The final “Diploma: Advanced School in AI” 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 successfully attended (title and duration), Project title, and Internship (when present)