Master in Software and Systems

Directed Study with Adviser

Lecturer (Coordinator):
Manuel Carro
mcarro@fi.upm.es
Lecturer:
Pedro López
pedro.lopez@imdea.org
Lecturer:
César Sánchez
cesar.sanchez@imdea.org
Lecturer:
Aleks Nanevski
aleks.nanevski@imdea.org
Lecturer:
Juan Caballero
juan.caballero@imdea.org
Lecturer:
Alessandra Gorla
alessandra.gorla@imdea.org
Lecturer:
Gilles Barthe
gilles.barthe@imdea.org
Lecturer:
Boris Köpf
boris.koepf@imdea.org
Lecturer:
Dario Fiore
dario.fiore@imdea.org
Lecturer:
José Morales
josef.morales@imdea.org
Lecturer:
Pierre Ganty
pierre.ganty@imdea.org
Lecturer:
Alexey Gotsman
alexey.gotsman@imdea.org

Semester

First semester

Credits

6 ECTS

Outline

This is a highly interactive and demanding subject. Students will be required to be in close contact with the adviser in order to make headway with the topic and material selected by the adviser, according to their research interests. Students will be required to frequently meet and interact with the adviser, often for as many as 2 to 3 hours per week, and devote a substantial amount of time to further researching the selected material. Thus, only very motivated students are advised to take this course after consultation with a prospective adviser.

Learning Goals

Syllabus

  1. Selection of a topic between the student and the instructor

Website

http://www.software.imdea.org/graduateschool

Prerequisites

Assessment Method

Tuition Language

English

Subject-Specific Competences

More information:

This table shows the code, description and proficiency level for each subject-specific competence

Code Competence Proficiency Level
SSC2 Analysis and synthesis of solutions to problems requiring innovative approaches to the definition of the computational infrastructure, processing and analysis of heterogeneous data types A
SSC7 Evaluation and application of diverse mathematical and statistical theories, and available knowledge extraction and discovery processes, methods and techniques for large data volumes A
SSC8 Application of the theoretical and mathematical foundations of heterogeneous functions and data processing and analysis and evaluation and design of related methods for application in practical domains A

Learning Outcomes

More information:

This table shows the code, description and proficiency level for each subject learning outcome

Code Learning Outcome Associated competences Proficiency level
RA-DIAP-4 Be familiar with examples of real applications and research trends and lines SSC2, SSC7 S
RA-DIAP-5 Be familiar with the theory of classical optimization methods and heuristics SSC8 A

Learning Guide

Subject learning guide for Directed Study With Adviser