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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 5 days ago
A wide range of offers to help you balance work and family life: on-campus kindergarten places, parent-child offices, etc. Further training opportunities and free in-house language courses Spacious
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project Relevant publications Relevant work experience Other relevant professional activities Curious mind-set with a strong interest in intrinsically disordered transcription factors Good language skills
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Materials Good language skills *************************************************************************** Application and Assessment Procedure Your application including all attachments must be in English
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Master’s degree with distinction or equivalent An IELTS (Academic) score of 6.5 minimum (with a minimum 6.0 in each component, or equivalent) for candidates for whom English is not their first language and
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atoms or similar. Curriculum vitae (CV) including information about your education, experience, language skills and other skills relevant for the position Original diplomas for Bachelor of Science and
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offered by the University Sports Institute (USI), the language courses offered by the ISI, the University Library and take advantage of the various canteens offering reasonably priced food. A summary of the
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, Mathematics or Engineering, with a solid background in machine learning and computer vision. You have excellent skills in applied mathematics, in probability theory, and a programming language (e.g., Python, C
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English is required since English is the official language of the program. The Max Planck Society is committed to increasing the number of individuals with disabilities in its workforce and therefore
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What you'll receive A stipend scholarship of $32,192 per annum for a maximum duration of 3.5 years while undertaking a QUT PhD. The duration includes an extension of up to 6 months if approved for your candidature. This is the full-time, tax-exempt rate which will index annually. You will...
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application of Large Language Models (LLMs) is potentially useful for software generation. This potentially enables faster and more effective coding by means of adequate prompting. In this research we aim