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• Participate in the department’s research environment • Complete a PhD training programme • Teach at one or more of the department programmes Your main task as a PhD student will be to develop and complete a PhD
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project under supervision • Participate in the department’s research environment • Complete a PhD training programme • Teach at one or more of the department programmes Your main task as a PhD
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At the Faculty of Engineering and Science, AAU Energy, a PhD stipend position is available within the general study programme. The position is offered in relation to the research group Underwater
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systems, large language models, agentic AI, or scientific software development, especially in industry or research settings. Strong software engineering skills, proficiency in Python programming, experience
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supervisor is Prof. Serafim Bakalis, Department of Food Science, E-mail: [email protected] , Direct Phone: +45 93 56 56 81. Qualifications needed To be eligible for the regular PhD programme, you must have
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Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research
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PhD position in detector development for the next generation of cryogenic dark-matter axion exper...
doctoral program at CERN, and will have access to experimental facilities at both institutions. The position includes a mobility component of up to two years at CERN, undertaken within CERN's Technology
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Within the European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Action (MSCA), University of Copenhagen is offering one PhD position for a Doctoral
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The Ministerial Order on the PhD programme (2013) and the Faculty’s rules on achieving the degree. Salary, pension and terms of employment are in accordance with the agreement between the Ministry of Finance and
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programming (particularly Python) and modern machine learning frameworks (e.g., PyTorch, TensorFlow). An interest in AI security, trustworthy ML, or the reliability of ML systems in safety-critical settings