As PhD Students you can attend all the courses of the University of Brescia.
Here you can find the list of the Engineering Courses, both bachelor and master degrees.
Subscription to the courses is mandatory. Moreover, you are strongly recommend to discuss your choices in advance with your PhD advisors and to present your study plan to the academic board.
| TDB |
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Mass media call them Autonomous Cars, meaning that they do not need a human driver; however, vehicles in traffic are never autonomous, they need to interact and cooperate with all the other vehicles and road users to drive you to destination and avoid crashes and accidents. The easiest way to cooperate is to communicate, so networking and communications are essential for the success of this extremely promising technology, that has been recently hampered by the first fatalities … all of them due to the lack of simple communication technology that would have prevented the tragic misjudgment of the situation by the driving algorithm.
This PhD course of 20 hours aims to present the more promising communication technologies for V2X (Vehicle to Anything) networking, from the 802.11p standard, to 5G-New Radio and network slicing, to Visible Light Communications, as well as the key functions needed to achieve cooperative driving and smart traffic management, including both safety related applications and traffic management.
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| TDB |
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| TBD |
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If Math is the language of science, Stochastic Math is the language of Advanced Modeling and Performance Evaluation, from Event Driven Simulation (in the very end nothing else than a Monte Carlo solution of a Semi-Markov Chain), to the analysis of Digital Communications and Computer Science, to Hidden Markov Chains that are one of the key ingredients of Computational Biology and of many applications of computational methods applied to Health Care, as well as one of the tools available in the Machine Learning zoo.
This course, while trying to be very “light” in advanced math use, wants to lay a very solid theoretical background to tackle any (well, almost any!) scientific problem than needs to deal with non-deterministic phenomena of simply under-determined systems where lack of knowledge appears as random behavior, from bugs in software to errors in transmission systems, noise in electronic devices, disease spreading, efficiency of medical treatment, and many more.
Modeling and Performance Evaluation are key ingredients of any engineering project, as well as Business Plans and Industrial Innovation: revenues for industries must be properly forecasted in advance (to decide if the initial investment is worth) and forecasting requires a model. A Stochastic Model simply adds details and probabilistic interpretation of future events, granting a much better understanding (compared to deterministic models) of the system under analysis and proper interpretation of risks and their consequences, from money loss, to risks for the environment and the society at large.
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| TDB |
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| 18 MAY 20 |
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Combinatorial optimization and graph theory have experienced a fast development over the last few decades. It is well known that a lot of problems in combinatorial optimization arose directly from everyday practice in engineering and management. Indeed all the topics considered in this course are motivated by pratical interpretations. The goal is to focus on some problems of combinatorial optimization which can be formulated and treated by graph theoretical methods. After an introduction into graph theory, we study some selected topics both from a theoretical and an algorithmic point of view.
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| 11 JUN 20 |
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| 21 JAN 20 |
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The goal of this course is to provide an introduction to principles and practical guidelines for mobile applications’ interaction design. Previous basic knowledge of interaction design, usability or mobile programming are desirable but not mandatory; the content of the course is in fact accessible to any graduated student in Engineering. The attendance of the course is suggested for those who plan to design and develop an interactive application to be used on mobile devices.
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| 23 JAN 20 |
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| 03 DEC 19 |
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Nowadays, research on reinforcement learning (RL) has demonstrated promising results in manifold domains, while major breakthroughs have been obtained in gaming applications (e.g., atari, GO, poker). In this short course, we will introduce the basic principles and algorithms for solving Markov Decision Processes and simple RL problems. We will further investigate how these algorithms have been extended, modified and applied at the state- of-the-art to solve challenging problems in the gaming/simulation domains, pointing at the open challenges in the research field. Finally, we will analyze the applicability of these algorithms with or without modifications in robotics.
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| 12 DEC 19 |
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| 04 FEB 19 |
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In this brief course, we give an introduction to deep learning and then we introduce a theory for modeling the agent interactions with the environments by means of the unified notion of constraint, that is shown to embrace machine learning and logic inferential processes within the same mathematical framework. Then, we present LYRICS (Learning Yourself Reasoning and Inference with ConstraintS), which can be regarded as a tool to assist the design of intelligent agents in a rich variety of application domains.
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| 08 FEB 19 |
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| 13 JUN 18 |
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The goal of this course is to provide an introduction to nanoscience and nanotechnology, the basics of preparation of nanomaterials and their characterization, together with the use of some of the required instrumentations. This course is suitable for graduate students with various engineering backgrounds.
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| 20 JUN 18 |
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| 09 JUN 17 |
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This short course will deal with the theory and practice of visual perception, with emphasis on techniques used in vision research and in visual information processing. Central areas of vision research including spatial vision, motion perception, color and binocular vision will be covered. Emphasis will be put on psychophysical methods. Anatomy and physiology, light and optics, convolutions and Fourier methods, and network theory and systems will also be examined when appropriate.
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| 17 JUL 17 |
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| 14 DEC 16 |
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Large-area flexible electronics can enable the emerging Internet of Everything applications where people, processes, data and devices will be integrated and connected, to augment quality of life. Using flexible substrates in combination with emerging organic and metal-oxides materials, significant progress has been made and nowadays low-cost flexible circuits, smart sensors and biomedical devices are available.
The goal of the course is to provide an overview of organic and metal-oxide materials, devices and fabrication technologies used for integrated large-area circuits, flexible sensors and bio-electronics.
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| 16 DEC 16 |
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| 06 DEC 16 |
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This course follows a practical perspective: it provides basic knowledge of conceptual modeling of data, by using a graphical language (UML), to design of simple databases. Secondly, from the conceptual model we will see how to design and implement concretely a database using common database management systems and how to extract knowledge by querying the database.
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| 13 DEC 16 |
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| 12 JUL 16 |
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The goal of this course is to provide an introduction to nanoscience and nanotechnology, the basics of preparation of nanomaterials and their characterization, together with the use of some of the required instrumentations. This course is suitable for graduate students with various engineering backgrounds.
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| 14 JUL 16 |
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| 14 JAN 16 |
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The goal of this course is to provide an introduction to the principles of optics and lasers, the basics of biology, the interaction of light with cells and tissues, and the applications of optical imaging and sensing techniques in biomedicine. This course is highly interdisciplinary and is suitable for graduate students with various engineering or biomedical backgrounds.
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| 09 FEB 16 |
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DRIMI COURSES (2020):
Additional courses for PhD students of all programmes:
