154 parallel-programming-"Washington-University-in-St" positions at Technical University of Denmark
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advice and provides educational programs and service to society. We are working to develop new environmentally friendly and sustainable technologies, methods and solutions, and to disseminate
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individuals. The department offers a wide range of courses and programs at bachelor, master and PhD levels across DTU's study programs. The department has 250+ employees with around half of the staff coming
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and competences across multiple engineering programs. To do so, you will: Develop the research field by identifying knowledge gaps of scientific, academic, and industrial relevance Contribute to and
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approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see
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Solid programming experience, preferably in Python Familiarity with structured data handling (e.g., SQL) and scientific workflows Documented experience with ontology development, knowledge graphs, and
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both. Mathematical maturity and experience with scientific programming are essential. A background in probabilistic methods is highly desirable, at the level of master’s courses in probability
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for phage display selections such as toxin purification from venoms. Requirements: Accepted for a MSc program at DTU. Previous working experience in a laboratory setting, e.g. in GMO class-1 labs is mandatory
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enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education . We offer DTU is a leading technical university globally recognized for the excellence
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Development: Collaborate with our team to develop compelling and competitive grant applications to grant programs such as Horizon Europe, LIFE, CETPartnership or others depending on availability and relevance
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, TESPy, or similar libraries. Strong programming skills in Python or MATLAB, including use of scientific libraries (e.g., NumPy, Pandas, Matplotlib, etc). Experience with machine learning (e.g., Scikit