37 computer-programmer-"https:"-"APOS-UFFICIO-CONCORSI-DOCENTI" "https:" "https:" "UCL" PhD positions in Luxembourg
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learning programme to foster our staff’s soft and technical skills Multicultural and international work environment with more than 50 nationalities represented in our workforce Diverse and inclusive work
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The Department of Finance at the University of Luxembourg invites students to apply for Doctoral researcher positions in Finance. The program follows the standard international format with rigorous
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research program that brings together physics, chemistry, and machine learning. Your research tasks will include: Uncertainty Estimation in Deep Neural Networks for MLFFs Implement and test uncertainty-aware
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Health. METAMIC 3 is funded by the European Commission under the Horizon Europe framework programme (Grant Agreement number 101225682). The METAMIC 3 project will embed Doctoral Candidates (DCs) in a
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of the BIVEC (the BeNeLux association for transport research) and as such it has free access to post-graduate courses also in other institutions in Belgium and the Netherlands. The training program
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within the project AI4TECS Writing a doctoral dissertation in computer science Publishing research findings in leading international conferences and high‑impact journals in AI, machine learning, and
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website: www.uni.lu/snt-en/research-groups/finatrax/ The selected candidate will be enrolled in the PhD program in Computer Science and Computer Engineering with specialisation in Information Systems (IS
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) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission of teaching and
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) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission of teaching and
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We are seeking a highly motivated PhD student to perform fundamental research and to conceive truly sparse solutions (on both, CPU and GPU) for dynamic sparse training, aiming to cut the training costs and energy requirements of state-of-the-art deep learning models significantly, while...