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., an Abstract of a publication or a one-page summary of your master thesis) as well as relevant certificates until 09 June 2024 using the link below. The application must not exceed 5 pages excluding certificates
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master’s thesis) as well as relevant certificates until 10 June 2024 using the link below. The first (online) job interview will take place on 18/19 June 2024. We encourage Master students to apply even if
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PhD position in “Economic Life Cycle Costing and Life Cycle Sustainability Assessment of Hydropower”
certificates until 10 June 2024 using the link below. The first (online) job interview will take place on 18/19 June 2024. We encourage Master students to apply even if they still have to graduate in the coming
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members (such as the supervisor of your master thesis/final project) who are willing to provide a recommendation letter at our request. Interviews will take place in two phases. If selected, there will be a
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researchers to present and publish their results at international conferences and in reputed journals. Requirements Specific Requirements MSc (masters) degree in Mechanical Engineering or equivalent, preferably
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of learning with limited data and computational resources, pertinent in areas such as autonomous systems, online resource allocation, and complex decision-making processes. Main Responsibilities: Conduct
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candidate who is driven by curiosity and has: -or will shortly acquire-, a Master degree, or equivalent, in Technical Medicine, Biomedical Engineering, (applied) Mathematics, Computer Science or a related
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as well. You have a solid background in systems and software security and have knowledge and skills in topics such as fuzzing, static analysis, and symbolic execution. You are an independent and original
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your background and experiences align with the requirements of this project Detailed Curriculum Vitae (CV): The CV, should include, if applicable, a list of publications; Bachelor and Master transcripts
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to staff position within a Research Infrastructure? No Offer Description The main goals of this PhD project are: Develop novel sparse training algorithms that improve the scalability and energy