Course description

Master 1 students start the year with a 3 to 4 weeks sequence, called Boot camp, which focuses on full-time programming training.

Course objective

Main objectives of the boot camp are:

• for students, to meet each other and build a group dynamic;
• that they understand the value of the Digital Sciences curriculum;
• the students are coming from various backgrounds and have different skill levels, so it is crucial to homogenize their computer skills and to give some basic theoretical bases for artificial intelligence.

Course description

This course will cover the main concepts of artificial intelligence. For each part of the course, after an individual learning and study on a specific use case, the students will have to present their solution to the problem they had to solve.

• Introduction, what is Data
• Supervised Classification
• kNN, Linear models, Decision trees, Naïve Bayes
• Model selection
• Regression
• Unsupervised learning
• Neural Networks and Deep Learning
• SVM, Ensembles (forest)
• Social Networks
• Text mining, Natural Language Processing
• Recommendation systems

Course objective

Students will be able to choose the best-fitted tools and algorithms in order to solve a particular problem.

Key concepts

Artificial Intelligence

Course description

This course will provide an advanced introduction to the field of data science, with a focus on network and spatial data. Topics will cover data project management, data collection, cleaning, analysis and visualization, network analysis and representation, and work with spatial data. The course will have a strong focus on hands-on sessions and personal projects.

Network part:

Why focus on network data? Over the past century, network studies have had significant impact in disciplines as varied as mathematics, sociology, physics, biology, computer science or quantitative geography, giving birth to Network Science as a field of itself. With the recent rise of social networks in the last decade, their use has now become widespread in the digital world. Here we will provide an introduction to the field of Network Science, from the theoretical foundations (generating, analyzing, perturbing networks) to the practical hands-on part (analysis and visualization of real-world networks).

Network topics covered in 2021-2022 (changes may be present in 22-23):

a. How to construct networks from real data?

b. How to analyze networks? (centrality measures, community detection, statistical analyses etc.)

c. How to visualise networks?

d. Dynamics and spreading phenomena on networks (epidemics / information spreading, diffusion)

e. How do networks wirings change in time? (network robustness, temporal networks)

f. How to represent more complex network data? Multilayer, multiplex networks.

Students will select, analyse and present a network of their choice as part of a personal project for the course. They will also choose an advanced topic in network science & big data for which they will make a presentation in a reverse classroom setting. They will in particular contribute to a wikipedia page about that topic.

Course objective

At the end of the course the students will have gained intuition to analyze real-world data. They will be able to use Python and R for statistical analyses and working with data. They will know practical tools and packages to work with spatial data, as well as network visualization tools. Finally, they will have obtained good practices for code and data management.

Skills

Knowledge skills

• Network theory
• Mathematical models
• Dynamic processes
• Algorithms
• Data analysis
• Data visualisation

Operational skills

• Network mining
• Network analysis
• Network visualisation
• Data curation

Cooperative skills

• Collaboration for personal and group projects
• Raise issues on github projects from other students
• Peer-evaluation
• Joint scientific presentations
• Peer-to-peer learning

Course description

Introductory course to robotics. Robotics is about building machines that can act in the real world and often substitute human action. Together, students and teachers will decipher how robots work and why engineers built them the way they are. The course will be based on hands-on manipulation of robots to learn basic concepts related to sensing, acting, and planning

Course objective

By manipulating robots, students will discover what robots are made of and understand how each component works. In small teams, students will program robots to complete simple tasks such as line following, obstacle avoidance, and automated drawing; which will help them understand some of the basic concepts of robotics: feedback loops, odometry, trajectory planning, direct and inverse model. The final project will be a robotic sumo competition.

Key concepts

Sensors; Actuators; Planning; Feedback loop; Odometry; Inverse model; Soft robotics

Skills

Knowledge skills

• Closed loop control
• Trajectory planning
• Odometry
• Inverse model
• Decision making
• Software interruptions

Operational skills

• C++ and Python programming
• Design of algorithms
• Practical understanding of the types of sensors and actuators available and their operating principle.

Reflective skills

• Understand the links between scientific concepts and disciplines
• Hypothesis-Prediction-Experiment-Test-Refinement Loop

Cooperative skills

• Team management
• Group problem-solving
• Collaborate together towards a common goal

Key concepts

Virtual experiences; Ubiquity; Immersion; Embodiement; New Media; Interactivity; Avatar and Bots; Design, products and tools; Ethical analytics; Modelisation

Skills

Knowledge skills

• Knowledge of the production steps of a digital work
• Understanding of the complementarity relationships between input and output sciences (sciences and social sciences)
• What do cultural and ethical approaches bring?
• Proficiency in specialized interactive media vocabulary
• Understanding of stakeholder tensions and communities

Operational skills

• Identify and organise accurate informations about a topic with relevant scientific publications
• Imagine a modification or a totally new versions of an immersive product
• Create a little virtual world with inhabitants

Reflective skills

• Analysis of a media system from the point of view of usage
• Identification of the ethical principles and philosophical conception underlying an immersive or playful experience
• Take a step back from the accompanying speeches and market expectations
• Imagine the untapped or under-tapped potential of a technology
• Understand the interactions between different media regimes

Course description

The class is in project mode. It starts with two project emergence sessions. 2 project review sessions punctuate the progress. A presentation of the documentation and an exhibition of the completed prototype conclude the work. The meetings with mentors are organized by each project team. The course leads students to carry out a personal, individual or group project. The rules are that the project:

• is a stage of the personal learning path, journey and helps to achieve a goal related to the personal and professional development project
• is a deepening of one of the Teaching Units, and to present explicit research aspects
• has a "tech for good" goal: contribution to the commons of knowledge, ethics by design, sustainable development, learning planet, in relation to the ideals and values of the CRI
• involving a relationship with a mentor and different resource persons, freely sought
• is interdisciplinary, and, optimally, has links with LeSc and LiSc
• leading to a functional and documented prototype

Course objective

• train in project management and design (roles, cooperation, roadmap, need and expectation of users, business model and value produced).
• give a space of freedom to deepen a field and specialize himself or herself
• develop an interdisciplinary cooperation
• develop personal relationships and address book
• fully experiment of the learning by doing concept
• enrich his-her skills portfolio
• assume all aspects of the documentation, to make the project shareable, transmittable, continuous and replicable

Key concepts

Documentation; Prototype; Workgroup; Skill portfolio; Path of learning; Value; Project; Design; Cooperation

Skills

Knowledge skills

• learn about an inclusive and sustainable innovation
• learn how to define needs and expectation, and document it
• search the relevant information (field research, interviews, technical tests, feedbacks…)

Operational skills

• lead a project, from idea to prototype
• organize a shareable documentation
• achieve a technical and social project, producing value for a living system

Reflective skills

• establish a relation between an open infrastructure, application or machine, and the achievement of social goals
• take into account the ecological footprint of digital projects
• be able to explain and justify technical and scientific choices, in front of various interlocutors

Cooperative skills

• share the work
• recognize the value of contributions, knowledge and know-how (their own and reciprocally that of others)

Course description

In recent years open science has lowered barriers to the flow of information amongst scientists, from scientists to the public, and most radically, has encouraged public input into scientific process, especially in the natural and health sciences. “Open science, open innovation, open to the world” are “the three O’s” proposed by the European Commissioner for Science Research & Innovation to think the next political agendas because science and innovation are playing an increasingly important role in shaping the future. What is today different from what it has ever been regarding science production and the demand of science with and for society is growing worldwide. What is inherently closed in the “scientific production factory” that has to be open and how to open it?

In the scientific organizations, much of the focus with open science is usually how to develop alternative open publication systems, or else how repositories can be made available to host all kinds of open data and content. On the one side, there is still strong dependency on tools and platforms run by commercial, corporate entities. On the other side, the characteristics of commons-based peer production systems persist with its unbalanced power relations.

Open science encompass also citizen science, understood as the involvement of community contributors in the research process. Non-researchers participate in some aspects, mainly data collection and processing. But as it evolves, citizen science projects have become more and more diverse and so is the contribution of the crowd, even parts of the research process are still not accessible.

The students will be invited to investigate the various ways by which open science and citizen science, serve research, learning and innovation for good, the revolution that it brings in the scientific organization and how to maintain open the research process.

Course objective

Students will be invited to experiment what is open science, through practice, familiarization with research projects experiencing different levels of openness to the crowd and group reflection. In doing so, they will be prepared to open their view on science and research, position themselves regarding the evolution of citizen science and innovation in the science.

At the end of the course, students will:

• be more familiar with the open publication system
• master and understand more deeply the steps of the research process
• reflect on what is science and dispose of tools to open scientific processes
• be prepared to extreme citizen science

Key concepts

Open science; Citizen science; Scientific process; Experimental process ; Scientific quality

Skills

Knowledge skills

• Familiarization with Open Access publishing and Open Access repositories
• Principles of Open Data management (analysis, use, and reuse of data), metadata, and data dissemination (sharing and granting access to data)
• Dissection of a research process and familiarization with professional research conduct which include research management skills, research integrity and ethics skills, and IPR and legal skills

Operational skills

• How to share code and data
• Collective intelligence

Reflective skills

• The impacts of opening science and the principle of equity, freedom and inclusivity
• The social role of science
• The ethics of science
• The knowledge economy

Course description

These courses will allow AIRE master students to learn around issues related to the use of computer tools in various fields. Students may also select one or more Collège de France’s inaugural courses in Computer Sciences, and underly explicit issues to present them to the rest of their classmates.

Course objective

The objective is to lead students to question the use of computer tools and their technological and societal repercussions.

Skills

Knowledge skills

• understand the role of computer tools in research
• identify the importance of computing in interdisciplinarity
• identify areas of knowledge and action concerned by IT

Operational skills

• Be able to analyse the content of a conference: guiding principle, key concepts, models, graphs, fields of application

Reflective skills

• Critical thinking

Cooperative skills

• conduct work group
• organize the division of roles and the complementarity of tasks
• time management (reading, discussion, setting up an analysis strategy, shared notes, collaborative writing, cross-referencing, P2P evaluation )

Course description

The course starts with an inventory of issues, coming from the students: what are the crucial aspects of digital transformation that require the vigilance of IT professionals and scientists ? What are the visions of digital and ecological transformation? What do we believe, hope, say, make, about the future of society? What do we learn from the IT practices, the observation of IT professional circles, the policy making and public debates?

Once the topics have been chosen, either by group or individually, students conduct their own research. They analyze books, articles, and interviews, and organize the results using the controversy method. Meetings with researchers and experts facilitate the work. The results are published and discussed with guests who best represent the topics covered.

Course objective

• Establish a space to think about "Science and society" issues
• Provide analytical tools from the humanities and social sciences
• Link this research to the construction of professional and citizen responsibility (Social- Scientific- Entrepreneur)
• Introduce "Ethics by design" into technical practices

Key concepts

Digital transformation; Climate change; Artificial Intelligence and society; AI for Humanity; Ethics by design; Future of work; regulation, fairness and transparency; Controversies studies

Skills

Knowledge skills

• In the field of digital transformation, to identify a controversy topic
• Collecting appropriated data (Reading, interviews, content analysis) to explore and explain
• Write a first literature review

Operational skills

• Know how to represent a complex problem with a mind map or a sketch note
• Design prospective scenarios (data and information-based)
• Carry out a demonstration or case study, to assess the impact of technology on societies (e. g. prove an algorithmic bias, measure an ecological impact, try to objectively assess the "ethical value" of a technology (TOU of a software or platform, bioinformatics, VR and health, Deep face and DeepFakes ...)

Reflective skills

• Question techniques and sciences with regard to the challenges of sustainable development
• Deliberate on the good matching of personal engagements and human values to technical practice.

Cooperative skills

• Have a behaviour open to "collective intelligence", explore the same topics with different interpretations, compare and cumule interpretations
• Modify his/her points of view, integrating new perspectives
• Build plural views, impossible to design without collective intelligence
• Learn to think from the other's place

Course description

The Advanced Computing module is organised as a set of different courses and workshops supervised by specialists so that students can have a theoretical and hands-on overview of important digital topics.

The topics covered can be fundamental subjects to know for computer scientists (modelisation, databases, security, network...) or much more advanced topics (quantum computing, blockchain, IoT...)

Students who wish to explore one or more specific themes will have later the opportunity to do so with a mentor during projects or internships.

Course objective

Students must acquire and develop the intellectual agility so they can discover and implement new concepts, either simple or advanced, in digital sciences.

Key concepts

IT concepts and practise: fundamental and advanced

Course description

This course allows students to review the knowledge required for project management:

• requirement analysis
• design thinking methodology
• specifications (analysis, writing, evaluation…)
• technical and business writing
• acceptance phase
• risk management
• agile methodologies
• quality management
• maintenance
• testing tools and methodologies
• cost, human resources, tasks, time management
• security, ethics by design
• managing a projects portfolio
• methodological and IT tools
• etc.

Research, product or service type projects will be discussed.

Course objective

Students will be familiar of the main lines of project management so they can apply what they have learned to their own projects or those in which they will participate.

This course will therefore include a theoretical but above all practical part, in the form of use cases, serious games or follow-up sessions on students' real projects.

The management of multiple projects will also be discussed.

Key concepts

Project management

Course description

Masterclasses will be organized with specialists in several disciplines.

Course objective

In addition to discovering or deepening the subjects discussed by the invited speakers, students will develop their skills related to the organisation of an event.

Key concepts

Event organisation; misc. subjects

• M1 students will perform an internship (of 4 months minimum) in France or abroad. This is their chance to apply what they learnt during their first semester intensive courses, to explore their passions, to gain precious experience and to learn how an interdisciplinary project is carried out in a real environment(R&D department, Research lab, NGO).

"Sustainable Sciences" is a 42-hour module for M1 students in the Université Paris Cité Master AIRE programme for the 1st term of the 2023/2024 academic year.

This module is organized in 2 parts:

• the problem space is designed around 2 axes: the presentation of a range of tools and methodological frameworks to objectify reflection, enabling, in particular, a critical look at the quality of information sources linked to environmental and societal issues, as well as an understanding of the non-negotiable parameters of planetary sustainability.

• the solution space combines 2 phases: a period of inspiration to learn how to "play differently", where certain players have succeeded in transforming a system to meet a societal challenge. Then it's the students’ turn to play, with a project and action component. You'll work with a multidisciplinary group to shed light on, understand and analyze a societal issue and its context. At the end of this investigation, you'll project possible solutions to the problem you've identified. During this project, you will be accompanied by mentors, and you will present the fruit of your research to the university community in a communicative and engaging format.

Course objectives: Identify the nature and complexity of environmental and societal issues. Apply methodological and thinking frameworks to consider the systemic nature of these issues. Carry out a sociological survey and produce a critical analysis. Integrate contradictory positions. Document a project. Present results and ideas in an engaging format.

This short course is conceived to guide students through their first steps in programming, specifically in using the Python software (which will be used by several courses of the first semester). No prior knowledge of programming is required. Only prerequisites are: file and folder browsing, basic logic (boolean operations), definition of a matrix, basic calculus.

Overview of the class: After an introductory discussion of the rationale of a probabilistic framework, we will briefly review the fundamental concepts of probability theory, and then develop and discuss commonly used methods for estimation and hypothesis testing (comparison of means, correlation analysis, regression, analysis of variance with parametric and non-parametric approaches). We will rely on rigorous but simple mathematical developments, putting emphasis on conceptual issues. In pace with the teaching sessions, in the practical sessions we will apply the tools discussed to example problems including real-world data. Possibility of extra-modules on advanced topics.

Course objectives: The course aims at developing the students' critical thinking and ability to formalize a scientific question in a probabilistic framework. While providing a broad coverage of practical tools for statistical analysis, the objective is to bring students to a level of autonomy in seeking the right tools for the problems they will encounter in their career.

required professional skills and tools to be able to manage their personal projects, learning and workload effectively. As a result of this course, they will get an understanding of the processes and activities of project management applied to individual work, hands-on experience in applying these theories in order to create a tailored project management system, as well as elements of effective collaboration.

Learning goals: Students will be able to design a personal project management system to organize their personal, learning and project work: identify and list tasks, resources, dependencies, … Choose a set of tools (digital and analog) to track and organize their workload Objective setting, evaluation and reflection on completion and effectiveness Managing stakeholders and communications for complex projects Collaborating effectively in complex settings Developing skills of self-awareness, reflexivity Adopting a scientific approach to task and project management: setting goals, designing experiments (course of action), deploy, evaluate and learn, adjust Knowing the specificities of the main project management schools (agile, waterfall, ...) and how to choose the most relevant methodology depending on the tasks at hand

Course Description

The goal of this course is to ensure that the students obtain broad training in the field of physiology and neuroscience. The course offers a rich experience that brings students to the forefront of research-based knowledge. Systems physiology and integrative neuroscience are intrinsically multi-disciplinary, spanning from molecular biology and gene regulation, to chemical and electrical signaling, and information processing. Each of these levels is combined to generate a mechanistic, molecular-to-systems level understanding of animal and human behaviours. Students will integrate approaches from various science disciplines to formulate a more global understanding of complex and integrated phenomena. Introductory lectures cover the basics of physiology, endocrinology and neuroscience. Special lectures cover specific hot topics in the field of physiology and neuroscience. Classes will be based on a hybrid system: frontal lectures, flipped classrooms and open discussions. A commitment to read articles and reviews is mandatory.

Systems biology is an attempt to understand cells (or other biological systems) as complex systems by studying their basic building blocks and their interactions, and connecting this reductionist view with a holistic, systemic perspective. To understand biological functions, it often does not suffice to study single proteins, single genes, single cells, or single organism. Rather, the ability of living matter to perform amazing tasks relies on an ensemble of actors acting collectively. Those tasks, involving for example decision making or information processing, can thus be understood only at the system level. The course will be grounded in quantitative biology and modeling motivated by examples coming from biological systems. In addition, we present some basic concepts of statistical physics and engineering that have been successful in solving such problems in other disciplines, and will show how they apply to the description of biological systems.

Course objectives:​ Gain knowledge about cells, cellular processes, and methods to study them. Be able to describe biological systems at the system level as a list of units and their interactions, or as a combination of modules, and the resulting dynamics. Become familiar with basic concepts of theoretical systems biology such as uncertainty, optimality, modularity, or robustness.

Course Description

Synthetic Biologists work to design and create biological systems that are scientifically interesting or technologically useful. Synthetic Biology adapts concepts from the physical and information sciences, seeking to become a fully rational framework for the engineering of life. This course will introduce the major trends that drive the Synthetic Biology literature. Whenever possible, we will identify the fundamental theories and practices that unify the field and provide a foundation for future projects. Students will confront an emerging domain with an unclear definition, vague boundaries, and boundless ambition. We will wrestle with uncertainty, diffused information, conflicting intuitions and multidisciplinarity. Course graduates will be prepared to design their own made-to-order systems, and will be familiar with the lab techniques required to bring them to life. The course includes no laboratory component. It is ideally complemented by a wet lab rotation in molecular biology or participation in the iGEM competition.

Explaining how biological systems function based on their underlying bimolecular reactions (systems biology approach) and interfering with or even reprogramming these cellular functions (synthetic biology approach) are two major objectives of biological research. Thanks to a long-lasting technological revolution, quantitative data are produced in increasing quality and quantity. Besides the qualitative interpretation of quantitative data, models are needed to make full use of the wealth of the available information.

So modeling approaches should help biologists to make a giant leap in their understanding on how biological systems work at the cellular level. Truth is that despite the huge potential, rational approaches have not yet delivered the expected results. Our capacity to predict the behavior of biological systems is surprisingly low in comparison to our prediction capacities for other complex systems such as aircrafts or computer networks. This is partly due to the fact that our "modeling technologies'' are not efficient enough for this class of systems. But this is also largely the mere consequence of the fact that we do know yet what really matters to quantitatively explain the behavior of even simple cellular processes.

Our vision is that in a not-so-distant future, models and quantitative approaches will play a central role in biology, to assess our understanding in a principled manner and possibly also to orient further research. The age of "quantitative observation" should be followed by the one of "quantitative understanding". This course has the ambition to improve your readiness to these upcoming changes.

In concrete terms, our goal is to introduce basic principles for model development ("the art of modeling") and few fundamental mathematical and computational tools to analyze, assess and revise dynamical models of cellular processes. We will focus on modeling gene expression and genetic regulatory networks. We will employ elementary tools from dynamical systems theory for model analysis and from optimization methods for model calibration. Lastly, we will consider how biological variability can be represented by and analyzed using models.

As the essential entry point to the program this course aims to introduce students in the main learning methodologies and theories. After this course students will be able to identify learning theories and to design learning experiences based on the approaches of the internationally established pedagogues. They will also be to adopt a pedagogical posture and to acquire the theoretical bases of educational history in an international level, to develop an understanding of the mechanisms of adults’ learning and to give them a chance to familiarize with education disabilities as well as develop skills for planification, implementation and evaluation of educational programs. Courses Importance The importance of this course is crucial for the successful completion of program of master studies in learning sciences. The information found above in the « Description » part will enable masters’ students to be autonomous and capable to accomplish educational projects and teaching activities in the area of their interest. Moreover, it will enable students to successfully develop a critical view of the learning and educational reality.

Course description: This course is divided into two parts:

• a first part that aims to give you the knowledge, design tools, postures and attitudes to design a course and then a training program. We will follow all the steps of designing a training program by working on real projects.
• A second part in the form of a hackathon where you will receive an assignment from a client and you will have to design the pedagogical path the most adapted to the needs of the learners and the client.

Course objectives: ● Identifying the differents steps in a program design process ● Discovering and using different design framework and tools to design a program ● Identifying the role, missions and skills of a program/learning designer ● Identifying the current key burning questions of a program/learning designer ● Identify the different types of assessment ● Designing a learning program

The emerging field of immersive technologies leads to new opportunities in various educational settings. The growing capacity and affordability of these systems arouse a particular interest within the industry and academia, which is driving the research and development of learning experiences enabled by these technologies. Today, eXtended Reality (XR) is used as an umbrella term that covers all of these different immersive technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR). Accordingly, this introductory course will present an overview of these technologies, supported by historical facts and real-world examples, and provide a comprehensive understanding of the digital transformation occurring in education and pedagogy as a result of these technologies. In addition, in this course, students will learn practical concepts and essential knowledge supported by core literature reviews on immersive technologies and discuss ethical issues and societal challenges linked to such technologies in learning. Moreover, students will be able to dive deeper into the field through discussions, in-class activities, and direct experimentation with materials made available by the Laboratory of Interdisciplinary Research on eXtended Reality (LIXR) within the Learning Planet Institute. This course will provide students with the opportunity to practice with the technology and develop simple immersive learning solutions to real-life problems related to learning.

Course objectives: ● Recognize extended reality technologies and key concepts related to them. ● Describe the spectrum of virtuality and distinguish key differences between immersive technologies. ● Discuss the use of XR in learning and training and recognize current research and market trends in this area. ● Classify and analyze the cognitive, affective, and psychomotor outcomes of immersive learning experiences. ● Argue ethical issues that may arise with the use of XR technology in learning and critique or defend the facts of the technology by considering societal challenges. ● Experiment and practice the technology using the technical documentation provided. ● Brainstorm, define and develop immersive learning solutions.

Course description

Communication skills are at the heart of scientific work. Communication is not a way to better «sell» research to funders, career enablers or the lay public. It is an intrinsic part of the research process itself: talking, listening, sharing, questioning… are communication activities which makes research possible. You don't learn science communication to communicate better, you learn science communication to do better research. This consideration is an underlying assumption of the whole course.

The first part of the course will be devoted to some training on how to talk to a scientific audience and to the lay public. Students will learn basic skills of science communication, how to structure a communication, the importance of identifying the key messages, etc. They will be evaluated based on a short presentation about their internship, given to their colleagues.

The second part of the course will explore the reverse of the communication process : how to listen to society at large for the benefit of research. We will address the following question : how can a (future) scientist learn how to listen to the voices of art, culture, politics, society, etc. in order to give a deeper meaning to his.her research work ? What are the skills that need to be developed ? How can we improve our research skills by learning to understand and interpret a book, a film, a survey of public opinion, an advertising campaign, a politician’s speech, etc ? Students will develop independent work in pairs or small groups, based on their research interests and cultural interests. They will have to present their analysis, reflections, and possibly practical realizations to the whole group.

Course Description Machine Learning (ML) and Natural Language Processing (NLP) have gone through significant changes in the past few years. In this course, we observe the foundations of Natural Language, both written and spoken, and the existing ML methods to parse, handle and understand it. We will practice various techniques: from classical NLP to state-of-the-art. We will cover topics such as unsupervised learning (topic modeling), document classification & information extraction from unstructured texts. The Course graduates will be prepared to approach various NLP tasks and combine them to solve complicated problems.

Course objective (Pedagogical objective) Have a deeper understanding of NLP terms and methods Be able to solve text-related tasks by converting them into a ML problems Prepare the students to continue staying up to date with the state-of-the-art through academic research

Course description: This course is divided into two parts:

• a first part that aims to give you the knowledge, design tools, postures and attitudes to design a course and then a training program. We will follow all the steps of designing a training program by working on real projects.
• A second part in the form of a hackathon where you will receive an assignment from a client and you will have to design the pedagogical path the most adapted to the needs of the learners and the client.

Course objectives: ● Identifying the differents steps in a program design process ● Discovering and using different design framework and tools to design a program ● Identifying the role, missions and skills of a program/learning designer ● Identifying the current key burning questions of a program/learning designer ● Identify the different types of assessment ● Designing a learning program

Overview of the class:

The general aim of the course is to introduce students to the cross-talk between physical and biological mechanisms in the regulation of basic biological processes. The course will focus on physical mechanisms that underlie the regulation of large-scale organization at the cell- and tissue level, notably cell polarity, cell division, and morphogenesis. Emphasis will be put on the cytoskeleton, on mechanical forces, and on diffusion. Half of the course will be dedicated to introduce basics of the cytoskeleton, reaction-diffusion, and methods to study them, and the other half will focus on discussing examples in different molecular, cellular and tissue contexts. Through the classes, students will learn about modern tools used in biophysics, such as quantitative imaging, force measurements and modelling, and their application to understand the spatial organization of a large range of molecular complexes, cell types and tissues. Key questions that will be addressed include: How do cytoskeletal elements generate forces? How are these forces integrated by cells to position organelles or generate forces at the cell or tissue scale? How do they contribute to determine cell shape and size? How are these processes regulated in different organisms, at different scales in space and time?

Goal of the class:

The course will provide students with a good knowledge of cytoskeleton regulation, and general biophysics. A number of classical experiments and techniques will be discussed. It will highlight differences between cell types, biological scales and conserved design principles or rules. Students will learn to observe, ask questions, formulate hypothesis and propose quantitative tests/experiments.