- Unit Coordinator: Marek Chmielewski
- ECTS Credits: 1
- Semester: 1
- Year: 2
- Campus: Gdansk University of Technology
- Aims:
The aim of the course is the answer on the question of ethics inluence on the accuracy of the science investigation procedure and presentation in the public results of the research and measurement results.
- Content:
The content of the course is the analysis and verification of existing codes of the ethics in the subjects of the research and development in science. Understanding and analyzing the ethic code in the field of nanotechnology. The analysis is also the history and evolution of content included within the applicable codex. In addition, the lecture will be analyzed as current controversial statements and publications in the field of science and especially nanotechnology.
- Reading list:
The Ethics of Nanotechnology, Andrew Chen
- Unit Coordinator: Alice Guyon, Ingrid Bethus
- Programme: InterMaths
- ECTS Credits: 6
- Semester: 1
- Year: 2
- Campus: University of Côte d'Azur
- Language: English
- Delivery: In-class
- Aims:
Neuronal and cognitive systems cannot be modeled without knowledge of the basics of Neurosciences, from the molecular to the integrated level, involved in cognition and behaviors.
The first part of the program focuses on elementary neurophysiology and neuroanatomy. What are the different subparts constituting the nervous system and what are their main roles? How are neurons constituted? How do they generate activity and communicate with other neurons?
The second part of the program explores, with an integrative perspective, the neurobiological basis for higher mental functions through several examples. Sensorimotor functions are at the root of all the other processes. So the study of feeding behaviors is a good way to learn about the bio-logic of elementary behaviors, starting from the physiology of the autonomic nervous system and ending with neuroethological issues. Learning and memory are the basic processes of higher mental functions and also hot topics with applications in many domains.
In addition to all these fundamentals, the course also explains the materials and methods used in cognitive neurosciences to obtain data at the different levels of organization of nervous, cognitive and behavioral systems. This course is taught by a teaching staff member of the Master Programme Mod4NeuCog at UCA.
- Content:
- Neuronal and cognitive systems
- Neurophysiology and neuroanatomy
- Neurobiological basis for higher mental functions
- Code: DT0317
- Unit Coordinator: Mattia D'Emidio
- ECTS Credits: 3
- Semester: 2
- Year: 1
- Campus: University of L'Aquila
- Language: English
- Aims:
Upon completion of this course the student will have reliably demonstrated the ability to design, analyze and implement algorithms for massive data sets using state-of-the-art algorithmic techniques in the area.
Furthermore, the student will be able to understand:
i) storage strategies that are suited for large-scale datasets (e.g. distributed, unstructured);
ii) alternative processing models that are relevant to big data;
iii) fundamentals of large-scale data mining.
Finally, the student will acquire basic knowledge of experimental algorithmic techniques and data analysis.
- Content:
Large-Scale Data Mining Models, Algorithms, Storage Techniques for Massive Datasets
- Pre-requisites:
Basic courses on design and analysis of algorithms and data structures. Mathematical and programming maturity. Fundamentals of data analysis.
- Reading list:
J. Leskovec, A. Rajaraman, J. D. Ullman. Mining of Massive Datasets. 2nd Edition.
- Unit Coordinator: Simone Fagioli
- ECTS Credits: 6
- Semester: 1
- Year: 2
- Campus: University of L'Aquila
- Language: English
- Aims:
- To learn the basics in the mathematical modelling of population dynamics.
- To provide a mathematical description of ODE models in population dynamics and the interpretation of the qualitative behaviour of the solutions
- To get the basic notions in mathematical models in epidemiology and reaction kinetics.
- To learn the mathematical modelling of population models in heterogeneous environment, described by partial differential equations.
- To deal with advanced models in biology such as chemotaxis models and structured dynamics equations.
- To get a sound background in reaction diffusion phenomena, Turing instability, and pattern formation.
- Content:
Continuous Population Models for Single Species. Continuous Growth Models. Delay models. Linear Analysis of Delay Population Models: Periodic Solutions.
Continuous models for Interacting Populations. Predator-Prey Models: Lotka-Volterra Systems. Realistic Predator–Prey Models. Competition Models: Principle of Competitive Exclusion. Mutualism or Symbiosis Time-space dependent models: PDEs in biology. Diffusion equations. Diffusion and Random walk. The gaussian distribution. Smoothing and decay properties of the diffusion operator. Nonlinear diffusion- Reaction–diffusion models for one single species. Diffusive Malthus equation and critical patch size. Travelling waves. Fisher–Kolmogoroff equation.
Reaction–diffusion systems. Multi species waves in Predator-Prey Systems. Turing instability and spatial patterns.
Chemotaxis modelling. Diffusion vs. Chemotaxis: stability vs. instability. Diffusion vs. Chemotaxis: stability and blow–up. Chemotaxis with nonlinear diffusion. Models with maximal density
Matematical models for tumor growth.
Structured population dynamics. An example in ecology: competition for resources. Continuous traits. Evolutionary stable strategy in a continuous model. - Pre-requisites:
Basic calculus and analysis (differential and integral calculus with functions of many variables).
Ordinary differential equations.
Basics in finite dimensional dynamical systems.
Elementary methods for the solution of linear partial differential equations (separation of the variables).
- Reading list:
Murray - Mathematical Biology I&II
Lecturers lecture notes
