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hold a PhD in oceanography, marine ecology, computer sciences, data sciences or similar. We expect that you have: Expert knowledge on network modelling, especially aimed at ecological applications Strong
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. Essential: Experience in sample preparation for cryoET studies and acquisition and analysis of cryo-EM data Knowledge of bioinformatics tools and data mining Good communication skills in English (oral/written
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willing to collaborate in an international and interdisciplinary team. Good communication skills in written and spoken English language are essential, and applicants should have a strong publication record
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with analysis of epidemiological data are required. Experience with modelling repeated measures within individuals is an advantage. As a person, you have good interpersonal skills, are inclusive and team
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thereafter. The position is a fixed-term full-time position for 24 months/ 2 years. The department of Biomedicine prioritises diversity and a good work environment, as this is a prerequisite for groundbreaking
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thereafter. The position is a fixed-term full-time position for 22 months. The department of Biomedicine prioritises diversity and a good work environment, as this is a prerequisite for groundbreaking research
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An ability to take initiative, develop, and manage research activities Proficient quantitative skills with data analysis and programming e.g. in R and python Documented experience in scientific writing and
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learning for imaging tasks Prior work with histology–imaging registration or material decomposition Clinical research exposure As a person, you have good interpersonal skills, are inclusive and team-oriented
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, experience with electrophysiological techniques both ex vivo and in vivo will be preferred. As a person, you have good interpersonal skills, are inclusive and team-oriented and able to contribute to a good
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and adaptive token pruning; Distributed and collaborative inference strategies; Mixture-of-Experts (MoE) architectures for scalable inference; Resource-aware and latency-constrained inference