O projeto de pesquisa da pesquisadora de pa³s-doutorado do Instituto Alberto Luiz Coimbra de Pa³s-Graduaa§a£o e Pesquisa de Engenharia (Coppe/UFRJ) Carolina Marcelino ébaseado em aprendizado de ma¡quina (machine learning), combinado com heurastica
A pesquisadora de pa³s-doutorado do Instituto Alberto Luiz Coimbra de Pa³s-Graduação e Pesquisa de Engenharia (Coppe/UFRJ) Carolina Marcelino foi agraciada com o Praªmio Marie Curie Fellow, concedido pela Unia£o Europeia. A condecoração éuma das mais cobia§adas distinções concedidas a jovens cientistas no mundo. Dos oito pesquisadores contemplados este ano, Carolina éa única representante das Amanãricas.
foto: Reprodução-Coppe/UFRJ
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)
Carolina atua na linha de pesquisa de Inteligaªncia Artificial, sob a orientação do professor Carlos Eduardo Pedreira, do Programa de Engenharia de Sistema e Computação (Pesc) da Coppe/UFRJ. A cientista recebera¡ um contrato no valor de € 4.500 mensais, por dois anos, para trabalhar na Espanha, além de recursos de bancada para desenvolver seu projeto de pesquisa. A premiação faz parte do Marie SkÅ‚odowska-Curie Actions (MSCA), um programa promovido pela Comissão Europeia, cujo objetivo éfinanciar pesquisadores promissores do mundo inteiro em diversas áreas do conhecimento.
“O projeto de pesquisa no qual irei trabalhar ébaseado em aprendizado de ma¡quina (machine learning), combinado com heurasticas de otimização e manãtodos de tomada de decisão para a geração de energia renova¡velâ€, explica Carolina, bolsista nota 10 da Fundação de Amparo a Pesquisa do Estado do Rio de Janeiro (Faperj). A pesquisadora ira¡ no pra³ximo semestre para a Universidade de Alcala¡, mas pretende manter os vanculos com os colegas da UFRJ. “a‰ uma conquista do nosso grupo e da Coppe, que éuma instituição de referaªnciaâ€, destacou.
“Estamos muito satisfeitos e orgulhosos, deixando evidente mais uma vez a excelaªncia internacional dos que trabalham em nosso programa na Coppeâ€, ressaltou o orientador.
Além de Carolina, foram agraciados com o praªmio, este ano, seis pesquisadores europeus e um chinaªs.