EMSSE - Erasmus Mundus Joint Master Degree and scholarships
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    Academic structure and content overview

    EMSSE is a new train­ing ini­tia­tive that seeks to pro­pose a com­plete pro­gramme cov­er­ing all Sys­tems Engi­neer­ing com­po­nents, with a spe­cif­ic empha­sis on sus­tain­abil­i­ty and innovation.

    The aca­d­e­m­ic path com­pris­es two aca­d­e­m­ic years (120 ECTS cred­its). Each year is split into two semes­ters, indi­cat­ed here­inafter as MxSy where Mx (i.e., M1 and M2) is referred to the year of study and Sy (i.e., S1 and S2) is referred to the spe­cif­ic semes­ter of that year. The pro­gram of the first year is devot­ed to estab­lish­ing a deep and joint­ly designed back­ground on SE top­ics. More­over, dur­ing M1S1, each HEI will offer lan­guage cours­es to be attend­ed by all stu­dents, where­as dur­ing M1S2, a course  will be devot­ed to pub­lic speak­ing, col­lab­o­ra­tive work­ing, and project-based learning.

    The sec­ond year is aimed at pro­vid­ing EMSSE stu­dents with a spe­cial­iza­tion relat­ed to three rel­e­vant SE areas. Then, in the sec­ond year, the course teach­ing is struc­tured around three aca­d­e­m­ic paths, cov­er­ing the fol­low­ing three main areas of sys­tem engi­neer­ing:

    Path1: SoSE — Systems of Systems Engineering led by UniGe

    This train­ing path address­es and analy­ses the meth­ods and tools for design­ing and imple­ment­ing meth­ods and soft­ware tools work­ing behind autonomous mutu­al­ly inter­act­ing sys­tems, i.e., sys­tems of sys­tems (SoS). These sys­tems gen­er­al­ly show capa­bil­i­ties relat­ed to per­cep­tion, com­mu­ni­ca­tion, learn­ing, deci­sion-mak­ing and action, and are able to inter­act with their envi­ron­ment and with sim­i­lar sys­tems. They must also han­dle numer­ous sources of uncer­tain­ty that can impact per­for­mance and, con­se­quent­ly, mon­i­tor the per­for­mance and the behav­ior of the whole sys­tem. More specif­i­cal­ly, this train­ing path aims at pro­vid­ing mod­el­ling and method­olog­i­cal approach­es to sens­ing, actu­a­tion, and con­trol in order to describe and analy­ses a sys­tem, and take deci­sions based on the avail­able data in a dis­trib­uted, pre­dic­tive and/or adap­tive man­ner, there­by per­form­ing “smart actions”. The stu­dent will approach such smart sys­tems by study­ing prop­er mod­els and meth­ods in dif­fer­ent appli­ca­tion con­texts, such as con­nect­ed autonomous vehi­cles and pla­toon­ing, drones, smart pow­er grids and ener­gy effi­cient build­ings, dis­trib­uted logis­tics, and envi­ron­men­tal mon­i­tor­ing. The first semes­ter of sec­ond year will take place at UNIGE, which have full poten­tial of exper­i­men­tal plat­forms and rec­og­nized research/teaching teams in this field.

    There are two mobil­i­ty paths for SoSE:

    • Mobil­i­ty path 1 (UTC-UPT-UniGe) ;
    • Mobil­i­ty path 2 (UPC-UPT- UniGe).

    Path 2: AOS — Advanced machine learning and Optimisation of Systems led by UTC

    This train­ing path focus­es on learn­ing theory/practice and opti­miza­tion, as applied to autonomous inter­act­ing tech­nol­o­gy-inten­sive sys­tems. The focus will be in deci­sion mak­ing and deci­sion sup­port in the con­text of com­plex sys­tems with advanced opti­miza­tion meth­ods also capa­ble of treat­ing the pres­ence of uncer­tain­ty. The appli­ca­tions will refer to ‘autonomous vehi­cles able to com­mu­ni­cate with each oth­er, to smart trans­port sys­tem infra­struc­tures, to air­borne mini drones, and to sen­sor net­works that exchange data in real time, to logis­tic ware­hous­es need­ing for pre­dic­tion real-time data analy­sis, sim­u­la­tion and opti­miza­tion. The plat­forms avail­able in the Heudi­asyc Lab­o­ra­to­ry (autonomous vehi­cle, vir­tu­al real­i­ty, rail­way sim­u­la­tor or mini drones) are at dis­pos­al of mas­ter stu­dents. The stud­ied sys­tems imple­ment machine learn­ing, deci­sion-mak­ing and action capa­bil­i­ties, while inter­act­ing with their envi­ron­ment and oth­er sys­tems. The first semes­ter of sec­ond year will take place at UTC, which is suc­cess­ful­ly expe­ri­enc­ing a mas­ter spe­cial­iza­tion track, com­bin­ing opti­miza­tion and machine learning.

    There are two mobil­i­ty paths for AOS:

    • Mobil­i­ty path1 (UPC-UPT-UTC) ;
    • Mobil­i­ty path 2 (UniGe-UPT-UTC).

    Path 3: AMS — Advanced Manufacturing Systems led by UPC

    Today’s soci­ety demands break­through tech­nolo­gies in emerg­ing areas as dig­i­tal indus­try and smart fac­to­ries. Fac­ing such chal­lenges requires that inter­dis­ci­pli­nary engi­neer­ing teams work togeth­er to come up with cre­ative, reli­able, eth­i­cal and sus­tain­able solu­tions. One of the key fac­tors in lead­ing suc­cess­ful projects is for pro­fes­sion­als from dif­fer­ent areas to have strong skills in mod­ern engi­neer­ing meth­ods such as big data, 3D print­ing, smart sen­sors, and com­put­er sim­u­la­tion. This train­ing path has been designed to enhance stu­dents’ aca­d­e­m­ic back­ground with such skills, thus prepar­ing them for the future. The spe­cial­iza­tions will allow them to face real prob­lems in the emerg­ing area of smart fac­to­ries. Dur­ing the train­ing path, plan­ning, sched­ul­ing and con­trol prob­lems will be for­mal­ized and solved accord­ing to the frame­work pro­posed by the ANSI/ISA-95 inter­na­tion­al stan­dard. Spe­cial focus will be devot­ed to the pri­ma­ry and sup­port func­tions giv­en by the Man­u­fac­tur­ing Exe­cu­tion Sys­tem (MES). At the end of the train­ing path, the stu­dent will be able to posi­tion an indus­tri­al automa­tion prob­lem in the con­text of ANSI/ISA-95 and to for­mal­ize and to solve deci­sion-mak­ing prob­lems, using prop­er meth­ods and tools. The first semes­ter of sec­ond year will most­ly take place at UPC which is ful­ly equipped for exper­i­men­ta­tion and have also a long suc­cess­ful expe­ri­ence on this area.

    There are two mobil­i­ty paths for AMS:

    • Mobil­i­ty path1 (UTC-UPT-UPC) ;
    • Mobil­i­ty path 2 (UniGe-UPT-UPC).

    In the last semes­ter M2S2, stu­dents have the oppor­tu­ni­ty to work on their Mas­ter The­sis in the most suit­able con­text for their project, which can be a “research lab envi­ron­ment” or a com­pa­ny in which the stu­dent can per­form an intern­ship period.