463 programming-"https:"-"https:"-"https:"-"https:"-"HFML-FELIX" positions in Netherlands
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course program tailored to your needs and the research team. The gross salary for the first year is € 3,059,- per month rising to € 3,881,- in the fourth year in according to the Collective Labour
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course program tailored to your needs and the research team. The gross salary for the first year is € 3,059.- per month rising to € 3,881.- in the fourth year in according to the Collective Labour
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for the overall implementation of the Vega and Space Rider programmes, including the Vega-C Competitiveness Improvement and Product Adaptation programmes and the Vega Evolution development programme. You will also
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scientific programming and an excellent command of English. Advantages (but not required) Background in systems and control. For example, familiarity with stability analysis, state-space methods, jump-flow
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implementation and application. Basic knowledge and a keen interest in physical problems (especially inverse problems) and scientific applications. Strong programming skills (preferably Python). Ability to work
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the requirements of the Belastingdienst (Dutch tax agency), and with our Dual Career Programme we will also help your partner find the right job for them. Employer Erasmus University Rotterdam (EUR) is
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programs in Rechtsgeleerdheid, Fiscaal Recht, Ondernemingsrecht, and Global Law (English-taught). Of the nine legal Master's programs, five are offered in a hybrid manner and six programs are English-taught
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University, we are investing in the next phase of our Bachelor's programme in Computer Science. We are looking for five Assistant Professors who are willing to contribute to the development of a young
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energetically with the vitality program. Will you need to relocate to the Netherlands for this job? TU Delft is committed to make your move as smooth as possible! The HR unit, Coming to Delft Service , offers
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Join the NWO Perspectief FIND program and develop methods to adapt Transformer-based foundation models for defect detection where data is scarce and unlabeled. Explore few-shot learning, self