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Field
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based at NHM’s interdisciplinary and highly international research group Evolution, eDNA, Genomics and Ethnobotany (EDGE) . Opportunities for networking across evolutionary biology, genomics, and
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especially LLMs, evidenced by publications and/or dissertation or equivalent evidence of expertise and completed research outputs Proven ability to write code in Python Experience working in a research team
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of linear wave equations and boundary element techniques as well as their high-frequency approximations. Furthermore, the candidate will be expected to extend the existing code in order to apply the developed
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optical instrumentation, software development, handling of biological samples and their preparation is required. Proficiency with microscopy, particularly FLIM and TPM, is highly desirable. Skills in coding
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referring to statistical and system characteristics in the real world contrary to an ideal learing setting. The candidate will contribute to understanding how neural networks extract the most relevant
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neural networks extract the most relevant information of the data to make a prediction using advanced mathematical tools. This insight opens the door for enjoying the real world. The candidate further
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Computer Science: Software Engineering, Sensor Networks and Engineering Computing (https://www.hvl.no/en/research/phd-programmes/phd-programme-in-computer-science/ ). The research programme in Computer Science
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, and collaborative international working environment that values diverse perspectives Access to a strong network of top-level national and international collaborators Research mobility funds supporting
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Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https
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, policy, energy conversion, new business models, techno-economic and life cycle analyses, machine learning, optimization, AI, intelligent networks, among others. The PDF will join a project studying