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can have a background in statistics, mathematics or agriculture, but must have experience with statistical modelling, generalized linear models, linear mixed models, and strong programming skills
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include: A letter motivating the application (cover letter) Curriculum vitae Grade transcripts and BSc/MSc diploma (in English) including official description of grading scale You may apply prior
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mathematical foundation of machine learning models. You will be responsible for developing scientific machine learning methodologies enabling new approaches for solving machine learning problems including
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, including electrical engineering, control theory, industrial engineering, electronics engineering, energy policy, data science, and applied mathematics. As part of the Alliance program, your project will be
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systems Documented knowledge in power and energy markets preferably Nordic and European markets Documented knowledge in dynamic modelling of power system Knowledge of mathematical optimization. Knowledge
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degrees in either the natural sciences (chemistry, physics, mathematical/computational biology) or in the formal sciences (statistics, computer science, mathematics), but must have a serious interest in
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algorithms. Graph Neural Networks. The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics or another field
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mathematics. A MSc degree in mathematics or a related field with strong mathematical content must be obtained before the starting time. Other desirable skills (but not required): Background in quantum computing
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modeling in LLMs with a focus on mapping these models to hardware. The research will address: Mathematical formulation and architecture of SSMs for language tasks Energy-efficient implementation in CMOS
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structured and mathematical mind-set, and a self-motivated drive to excel in research. Prior experience with research in photonic crystals, band topology, or symmetry analysis is an advantage. Only candidates