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

Reports a new list whose items are the same as in the input list, except that if two or more equal items appear in the input list, only the last one is kept in the result.

ca:elimina els duplicats de _ Reports a sorted version of the list in its first input slot, using the comparison function in the second input slot. For a list of numbers, using < as the comparison function will sort from low to high; using > will sort from high to low.

ca:ordena _ segons criteri _ The second input is an "association list," a list of two-item lists. Each of those smaller lists has a "key" as its first item and a "value" as its second. ASSOC reports the first key-value pair in the association list whose key matches the first input.

ca:associació _ _ This block carries out the given script for each item of the given list, like the primitive FOR EACH. What's different is that it provides the # variable, which will contain the item number in the list of each item in turn, 1 while processing item 1, and so on.

ca:_ per cada _ de _ _

1Takes a (possibly deep) list as input, and reports a human-readable text form of the list (namely, Lisp notation). Will not work on circular lists.

ca:notació textual de _ ( )SENTENCE is the main constructor for sentences, represented as lists of words. It takes zero or more inputs, each of which can be either a list or a text string. If a list, the input is assumed to be a list of words. If a text string, it is converted to a list of words using SENTENCE→LIST. Then all the lists of words are appended to form a new list of words. If the inputs are lists of lists rather than lists of words, SENTENCE, like APPEND, does only one level of flattening, reporting a list of all the items of all the input lists.

ca:frase _ ZIP takes any number of lists as inputs. The lists should all be the same length. ZIP reports a list of lists in which the first item is a list of all the first items, the second item is a list of all the second items, etc. Viewing the inputs as the rows of a matrix, ZIP reports its transpose.

The identity function reports its input.

Takes as input a function of N inputs and N lists. The function is called with item 1 of all the lists as its inputs, with item 2 of all the lists as its inputs, and so on. (The lists should all be the same length.)

ca:multi-mapeja _ sobre _ Takes a sentence in text string form and reports the sentence as a list of its words.

de:Satz $arrowRight Liste _ ca:frase $arrowRight llista _ es:frase $arrowRight lista _ fr:phrase $arrowRight liste _ pt:uma lista com as palavras da frase _ 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CAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIEAB/8+bAAAFmklEQVSAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAgIED8AAECBAgQIECAAAECmYAAyagNESBAgAABAgQIECAgQPwAAQIECBAgQIAAAQKZgADJqA0RIECAAAECBAgQICBA/AABAgQIECBAgAABApmAAMmoDREgQIAAAQIECBAg8MpvAllhbH9NAAAAAElFTkSuQmCCSaps qui és aquesta científica?Magda Campins5175Saps qui és aquesta científica?Anna Veiga Lluch5200Saps qui és aquesta científica?Carme Jordi i Nebot5250Saps qui és aquesta científica?Pilar Bayer Isant5165Saps qui és aquesta científica?Gemma Marfany6210Saps qui és aquesta científica?Enriqueta Felip6190Saps qui és aquesta científica?Núria Lopez Bigas5150Saps qui és aquesta científica?Carme Torras Genís5220Saps qui és aquesta científica?Sílvia Osuna5200Saps qui és aquesta científica?Marta Prevosti55Nom,ReconeixementMagda Campins,"Metgessa i epidemiòloga, investigadora del grup d'Epidemiologia i Salut Pública del Vall d'Hebron Institut de Recerca i presidenta del Comitè Científic Assessor de la Covid. També és professora de medicina preventiva i epidemiologia a la Facultat de Medicina (UAB). Guanyadora Premi ACRA 2021, per a la millora del benestar i la qualitat de vida de les persones, en la categoria de “millor trajectòria professional”"Anna Veiga Lluch,"Biòloga, investigadora i professora universitària; especialitzada en reproducció assistida, embriologia clínica, genètica reproductiva i bioètica; així com en l'estudi de les cèl·lules mare i les seves aplicacions clíniques en el tractament de les malalties degeneratives. Ha publicat més de 280 articles científics en revistes nacionals i internacionals. Distinció Creu de Sant Jordi 2004 o la Medalla Narcís Monturiol al Mèrit Científic i Tecnològic. "Gemma Marfany,"Especialitzada en genètica molecular, estudia les malalties humanes hereditàries. Cofundadora d'una empresa spin-off dedicada al diagnòstic genètic de malalties hereditàries de la visió: DBGen Ocular Genomics. Guardonada amb la VI Distinció, atorgada conjuntament pel Claustre de Doctors i pel Consell Social de la Universitat de Barcelona a les millors activitats de divulgació científica i humanística."Silvia Osuna,"Química computacional especialitzada en el desenvolupament d'enzims a la carta per a la síntesi i la producció de fàrmacs. Desenvolupa simulacions per ordinador i eines per al disseny de nous enzims d'interès industrial, que poden accelerar les reaccions químiques. És investigadora ICREA de la Universitat de Girona, on dirigeix el grup CompBioLab de l'Institut de Química Computacional i Catàlisi (IQCC)."Carme Jordi i Nebot,"Doctora en Física per la Universitat de Barcelona. La seva carrera científica va començar en el camp de l’astrometria de precisió i sistemes de referència. Paral·lelament, va treballar com a tècnic de sistemes en el sector privat i en 1991, es va incorporar com a professora titular en la UB. Pertany a l'Institut d'Estudis Espacials de Catalunya, a l'Institut de Ciències del Cosmos i a la Unitat Associada amb l'Institut de Ciències de l'Espai del CSIC. La seva activitat científica inclou la determinació de paràmetres fonamentals d'estrelles i cúmuls estel·lars. Es va involucrar en les missions espacials Hipparcos i Gaia de l’ESA, per la seva rellevància. Actualment, és co-IP del projecte de REPTES ESP2016 del grup Gaia-Barcelona amb 25 científics i enginyers."Pilar Bayer Isant,"Matemàtica acadèmica de la Reial Acadèmia de Ciències i Arts de Barcelona des de 2001, de la Reial Acadèmia de Doctors de Catalunya des de 1996, i és membre de l'Institut d'Estudis Catalans, des de 2001. El 1968 es va llicenciar en matemàtiques a la Universitat de Barcelona i el 1975 es va doctorar amb la tesi 'Extensiones maximales de un cuerpo global en las que un divisor primo descompone completamente'. Ha treballat com a professora de matemàtiques a la Universitat de Barcelona i a la Universitat Autònoma de Barcelona. El 1982 va obtenir la càtedra d'àlgebra de la Universitat de Barcelona. Ha creat un important grup de recerca en teoria de nombres. Les seves publicacions versen sobre funcions zeta, formes automorfes, el problema invers de la teoria de Galois, equacions diofàntiques i corbes de Shimura."Enriqueta Felip,"Oncòloga llicenciada en Medicina i Cirurgia per la Universitat Autònoma de Barcelona, on es va especialitzar en Oncologia Mèdica i va assolir el seu Doctorat. Té una àmplia trajectòria professional i acreditada experiència en el sector de l'oncologia, així com coneixements en l'àmbit científic i d'investigació. Doctora Cum laude, dirigeix la Unitat de Càncer de Pulmó i la Unitat de Càncer de Cap i Coll de l'Institut Oncològic Baselga. Al seu torn, és cap de Secció i responsable de la Unitat de Càncer de Pulmó del Servei d'Oncologia de l'Hospital Vall d'Hebron. Membre actiu de nombroses associacions de l'especialitat nacionals i internacionals, com la Societat Americana d'Oncologia Clínica (FÀSTIC) i l'Aliança Nacional contra el Càncer de Pulmó nord-americana entre altres. Ha publicat més de 70 articles sobre tumors toràcics per a prestigioses revistes científiques. "Marta Prevosti Monclus,"Arqueòloga membre de la Secció Històrico-Arqueològica de l'Institut d'Estudis Catalans. És investigadora de l'Institut Català d'Arqueologia Clàssica, al qual es va incorporar l'any 2014, després de quatre anys de dirigir el Museu de l'Estampació de Premià de Mar. Ha centrat la seva recerca en els estudis de territori i poblament rural dins del marc de la ciutat romana i s'ha especialitzat en la topografia arqueològica. Ha publicat més d'un centenar de llibres i articles científics. Abans, havia estat professora d'Arqueologia de la Universitat de Barcelona. Entre els anys 1982 i 1985 va codirigir les excavacions de la vil·la romana de Torre Llauder, a Mataró, que van restaurar uns mosaics localitzats durant la dècada de 1960 i van excavar nivells inferiors, l'anàlisi dels quals va permetre una seriació cronològica fiable."Núria Lopez Bigas,"Biòloga especialitzada en la genòmica computacional del càncer. És professora en Institució Catalana d'Investigació i Estudis Avançat (ICREA) a la Universitat Pompeu Fabra (UPF), on ha sigut cap del grup d'investigació en genòmica biomèdica del Departament de Ciències Experimentals i de la Salut (DCEXS-UPF), al Parc d'Investigació Biomèdica de Barcelona (PRBB), durant més de 10 anys. La seva investigació se centra en el desenvolupament d'eines computacionals que ajuden a la identificació de mutacions causants de tumors. Algunes de les aportacions del seu grup de recerca han sigut el descobriment de perquè en alguns tipus de tumor, s'acumulen mutacions en llocs específics del genoma, o la descripció de les mutacions de més de 4.000 tumors originats en 13 òrgans diferents. Actualment, dirigeix el seu grup en l'Institut d'Investigació Biomèdica (IRB)."Carme Torras Genís,"Matemàtica i escriptora especialitzada en intel·ligència artificial i robòtica. Compagina l'escriptura literària amb la investigació científica. És, actualment, professora d'investigació a l'Institut de Robòtica (CSIC-UPC). És membre numerària de l'Academia Europaea i membre numerari de la Reial Acadèmia de Ciències i Arts de Barcelona. Està llicenciada en matemàtiques i té un doctorat en informàtica. És membre de l'Associació d'Escriptors en Llengua Catalana i de la Societat Catalana de Ciència-Ficció i Fantasia. Compta amb una sèrie de guardons, entre els quals destaquen el Premi Divulga del Museu de la Ciència de Barcelona, el Premi Rafael Campalans de l'Institut d'Estudis Catalans i la Medalla Narcís Monturiol de la Generalitat de Catalunya."00