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, particularly joint action studies strong programming skills in Python Approval and Enrolment The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one
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including GNNs, generative models, or foundation models). Scientific software development, high-performance computing and data infrastructure. Are comfortable programming in Python and working with modern
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the following areas: Knowledge of computer science and machine learning. Familiarity with electrical and electronic engineering. Proficiency in programming languages such as Python, C++, or MATLAB. Experience
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Designer, BioSTEAM or similar) is an advantage. Excellent communication skills in English, and willingness to work in an international, interdisciplinary team. Openness to research visits and collaboration
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their main activity (work, studies, etc.) in Denmark for more than 12 months in the 3 years immediately prior to the date of appointment. Fluency in written and spoken English is a requirement. A master’s
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offered within the Marie Skłodowska-Curie QuNEST – Quantum Enhanced Optical Communication Network Security doctoral training program (https://qunest.eu ) and the Villum Investigator Program: Power-Efficient
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mandatory: Experience with bioinformatics including NGS (Nanopore, Illumina, PacBio) Experience with automation and coding in Python or other programing languages Experience with protein software tools like
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of the Marie Skłodowska-Curie program, the applicant must not have resided or carried out their main activity (work, studies, etc.) in Denmark for more than 12 months in the 36 months immediately before their
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, to plan and carry out complicated tasks, including laboratory work Motivation to work in interdisciplinary and sustainability-focused research on waste valorization You must have a two-year master's
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, visit: www.algoritmer.org . The candidate will conduct teaching as part of the ph.d. programme. You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level