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Titlebook: Jobinterviews professionell führen; über 400 Interviewfr Christof Obermann,Marc Solga Book 2018 Springer Fachmedien Wiesbaden GmbH 2018 Aus

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發(fā)表于 2025-3-21 16:03:46 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Jobinterviews professionell führen
副標(biāo)題über 400 Interviewfr
編輯Christof Obermann,Marc Solga
視頻videohttp://file.papertrans.cn/502/501003/501003.mp4
概述Alles, was Personalverantwortliche für erfolgreiche Einstellungsinterviews wissen müssen.Das Handwerkszeug für die Gestaltung von Auswahlinterviews für treffsichere und zuverl?ssige Auswahlentscheidun
圖書封面Titlebook: Jobinterviews professionell führen; über 400 Interviewfr Christof Obermann,Marc Solga Book 2018 Springer Fachmedien Wiesbaden GmbH 2018 Aus
描述Eine ?u?erst hilfreiche und fundierte Lektüre für erfolgreiche Einstellungsinterviews und professionelle Kandidatenauswahl..?.Führungskr?fte und Personaler erhalten hier das Handwerkszeug, um Auswahlinterviews aussagekr?ftig zu gestalten und so zu treffsicheren und zuverl?ssigen Auswahlentscheidungen zu gelangen. Unn?tige zeitliche und finanzielle Belastungen, die durch falsche Einstellungsentscheidungen entstehen, lassen sich hierdurch vermeiden.?.Den Kern des Praxishandbuches bilden rund 400 Interviewfragen zu 65 unterschiedlichen Kompetenzen. Führungskr?fte und Personaler erhalten für fast alle Berufe valide Interviewfragen, die sie direkt in Auswahlgespr?chen verwenden k?nnen..
出版日期Book 2018
關(guān)鍵詞Auswahlinterviews für Führungskr?fte und Personaler; Interviewfragen; Bewerberauswahl; Psychologie der
版次1
doihttps://doi.org/10.1007/978-3-658-18714-9
isbn_softcover978-3-658-18713-2
isbn_ebook978-3-658-18714-9
copyrightSpringer Fachmedien Wiesbaden GmbH 2018
The information of publication is updating

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發(fā)表于 2025-3-21 22:36:34 | 只看該作者
and hindering deployment to high-risk tasks. Causal confusion refers to a phenomenon where an agent learns spurious correlations between features which might not hold across the entire state space, preventing safe deployment to real tasks where such correlations might be broken. In this work, we exa
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發(fā)表于 2025-3-22 04:52:23 | 只看該作者
Christof Obermann,Marc Solgarawing inferences from symbolic knowledge). Many methods in the literature support injection of symbolic knowledge into predictors, generally following the purpose of attaining better (i.e., more effective or efficient w.r.t. predictive performance) predictors. However, to the best of our knowledge,
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發(fā)表于 2025-3-22 11:58:42 | 只看該作者
Christof Obermann,Marc SolgaIn particular, AI-based persuasive technologies are designed to steer/influence users’ behaviour, habits, and choices to facilitate the achievement of their own - predetermined - goals. Nowadays, the inputs received by the assistive systems leverage heavily AI data-driven approaches. Thus, it is imp
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發(fā)表于 2025-3-22 18:29:12 | 只看該作者
Christof Obermann,Marc SolgaIn particular, AI-based persuasive technologies are designed to steer/influence users’ behaviour, habits, and choices to facilitate the achievement of their own - predetermined - goals. Nowadays, the inputs received by the assistive systems leverage heavily AI data-driven approaches. Thus, it is imp
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發(fā)表于 2025-3-22 22:58:35 | 只看該作者
Christof Obermann,Marc Solgar the tumor is caused by HPV infection (HPV status) is essential to select the appropriate treatment. Therefore, developing non-invasive, transparent (trustworthy), and reliable methods is imperative to tailor the treatment to patients based on their status. Some studies have tried to use radiomics
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發(fā)表于 2025-3-23 01:25:27 | 只看該作者
ttings - where transparency and accountability play important roles in automation - is challenged by methods’ limited ability to provide explanations. Among the paradigms for explainability in DRL is the interpretable box design paradigm, where interpretable models substitute inner closed constituen
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發(fā)表于 2025-3-23 07:36:33 | 只看該作者
Christof Obermann,Marc Solgattings - where transparency and accountability play important roles in automation - is challenged by methods’ limited ability to provide explanations. Among the paradigms for explainability in DRL is the interpretable box design paradigm, where interpretable models substitute inner closed constituen
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