11 computer-engineering-"https:"-"https:"-"https:"-"CESBIO" PhD research jobs in Luxembourg
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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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, Financial Technology (Fintech), Energy Informatics, Energy Economics, and Consumer & Behavioural Research. In particular, they will be integrated into a larger research team and collaborate with team members
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The University of Luxembourg invites applications for a fully funded doctoral position in mathematical and computational modelling within the framework of the doctoral training unit Forest Function
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! Education · PhD in computer science, engineering, applied mathematics, physics, or another STEM discipline. · Demonstrated experience with mathematical and numerical optimization methods
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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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Alzheimer’s and Parkinson’s. You will work within a multidisciplinary environment alongside data scientists, software engineers, biomedical researchers, and clinicians. Your research will focus on developing AI
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usual way of working and the language of communication is English. Our current team members are spread across three universities (University of Luxembourg, Eindhoven University of Technology, and
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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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group with broad research competences and interests, with expertise covering mathematics, engineering, computer science and social sciences. We offer excellent working conditions in an international and
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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