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UID:89270-1776297600-1776470399@welcome.unicaen.fr
SUMMARY:Transnitria under Romanian and Nazi occupation (1941–1944)
DESCRIPTION:Version française \n\n\n\nTransnistria — between the Dniester and Bug rivers under Romanian and Nazi occupation (1941–1944)— was a complex and understudied space where genocide and occupation practices were tested. Jews\, Roma\, and local populations navigated shifting conditions of violence and survival. \n\n\n\nThis workshop will bring together international scholars and aims to: (1) analyse violence and survival in their local contexts through microhistorical approaches; (2) explore how survival strategies were shaped by gender\, age\, and social networks; and (3) examine how these experiences were reinterpreted after the war and influenced postwar justice and social relations. \n\n\n\nDrawing on testimonies\, legal records\, and archives\, it investigates everyday survival and situates Transnistria within broader debates on the Holocaust in Eastern Europe\, while fostering future collaborations. \n\n\n\nThe workshop is organized by Marie Moutier-Bitan (MRSH/ERLIS) and Svetlana Suveica (Leibniz Institute for East and Southeast European Studies (IOS)\, Regensburg)\, in partnership with the Fondation pour la Mémoire de la Shoah and the Deutsche Forschungsgemeinschaft (DFG). \n\n\n\n\n\n\n\n\nShare
URL:https://welcome.unicaen.fr/event/transnitria-under-romanian-and-nazi-occupation-1941-1944/
LOCATION:Caen · Campus 1 · Maison de la recherche en sciences humaines (MRSH)\, Campus 1\, Maison de la recherche en sciences humaines (MRSH)\, 1 Esplanade de la Paix\, Caen\, 14000\, France
ATTACH;FMTTYPE=image/jpeg:https://welcome.unicaen.fr/wp-content/uploads/sites/50/2026/04/Workshop-Holocaust-Chair.jpg
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DTSTART;TZID=Europe/Paris:20260528T110000
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UID:89316-1779966000-1779971400@welcome.unicaen.fr
SUMMARY:Laboratory discovery of new phosphors assisted by machine learning
DESCRIPTION:Conference by Romain Gautier\, CNRS Research Fellow\, as part of the “AI and Science” lecture series \n\n\n\nArtificial intelligence (AI) and sciences maintain rich and constantly evolving connections\, opening up unprecedented opportunities for research. What are the concrete applications of AI for research\, and what impact does AI have on scientific practice and on researchers? Conversely\, how do the sciences inspire the evolution of AI? \n\n\n\nThe lecture series dedicated to AI for science explores these questions. These lectures are intended for all researchers and faculty researchers\, PhD students\, and members of higher education and research institutions. A wide range of topics will be covered\, from theoretical foundations to practical use cases. \n\n\n\nRomain Gautier\, CNRS Research Fellow at the Institut des matériaux de Nantes Jean Rouxel\, will give a lecture entitled “Laboratory discovery of new phosphors assisted by machine learning” (in English): \n\n\n\nThe design of materials with specific characteristics is a complicated task as very small modifications in the chemistry or crystal structure of materials can have drastic effects on the physical properties. For example\, the presence of dopants/defects in very low concentrations combined with different phenomena make\, in most cases\, the properties difficult to predict prior to synthesis and characterization. In this context\, we use machine learning approaches to guide the discovery of materials. After a brief introduction on the potential of machine learning in materials science\, I will present two families of materials that we recently investigated using these tools: (i) the lanthanides doped silicates\, and (ii) the hybrid metal halides (including hybrid perovskites). Such materials can exhibit broad‐ or narrow‐band light emissions and relatively high photoluminescence quantum yields. The machine learning tools were used to identify the key experimental parameters to design phosphors with specific photoemission colors\, to optimize the color rendering for phosphors with tunable correlated color temperature\, or to automatically identify key structure types from the powder X-ray diffraction patterns of new compounds. \n\n\n\n\n\n\n\n\nShare
URL:https://welcome.unicaen.fr/event/laboratory-discovery-of-new-phosphors-assisted-by-machine-learning/
LOCATION:Caen · Campus 2 · École nationale supérieure d’ingénieurs de Caen (ENSICAEN)\, 6 Boulevard Maréchal Juin\, Caen\, 14000\, France
ATTACH;FMTTYPE=image/jpeg:https://welcome.unicaen.fr/wp-content/uploads/sites/50/2026/05/artificial-intelligence.jpg
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