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requirements To fulfil the general entry requirements, the applicant must have qualifications equivalent to a completed degree at advanced level (second-cycle), or completed course requirements of at least 240
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PhD position in Experimental Physics with focus on photonics and materials science (applied aspects)
research environment within a prestigious European training network. Competence requirements To be admitted for studies at third-cycle level, applicants are required to have completed a second-cycle level
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to two PhD students in Statistics for the research project “Next-Generation Latent Variable Models for Social Data Science”. Application deadline: November 4, 2026. We are seeking one to two PhD students
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for parts of the research project. Admission requirements To be admitted to doctoral education, applicants must meet the general and specific entry requirements described below and be considered to have the
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, for applications such as quantum-secure and post-quantum cryptography, and leakage- and tamper-resilient cryptography. It is supervised by Asst. Prof. Mustafa Khairallah. The research is expected to include a
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the three years immediately before the application deadline of 15 October 2026 (Marie Sklodowska-Curie mobility rule). Merits are: experience from protein production and purification, and/or Python code
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The doctoral student will be admitted to study the third-cycle subject Mathematics. To fulfil the general entry requirements, the applicant must have qualifications equivalent to a completed degree at the second
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September 15, 2026 or as agreed. The application deadline is August 15, 2026. Project description The project aims to generate new knowledge on how households interact with energy systems in a sustainable
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when you submit your application. Additional required qualifications are: A suitable candidate for this four-year project is meticulous, with logical thinking and a strong problem-solving ability. Good
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or by agreement. Application deadline is 16th August 2026. Description The PhD project will investigate how marine observation systems can become more resilient, scalable, and adaptive under accelerating