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forward allele-frequency diffusion and backward genealogical merging processes. The PhD candidate will work at the interface of machine learning, statistics, probability, and with applications in
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forward allele-frequency diffusion and backward genealogical merging processes. The PhD candidate will work at the interface of machine learning, statistics, probability, and with applications in
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experience with imaging, image processing, and/or visualization, as well as excellent programming skills. You must have a relevant Master's degree in computer science, electrical engineering, imaging science
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complex biological systems. Research Environment & Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable
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inventories and provision of environmental information. Similarly, the developments in AI and machine learning allow for new and improved processing of remotely sensed data supporting precision forestry
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candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable implementations. By establishing a new class of multi-frame factorization methods
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. Experience with control and synchronization of high-speed imaging and lighting systems. Experience with image post-processing and data extraction. Personal characteristics To complete a doctoral degree (PhD
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complementary and synergic methods at the intersection of Artificial intelligence, Machine learning, Numerical simulation, Formal verification. Such methods include, among the others: AI-guided simulation
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Computer science » Computer systems Computer science » Programming Technology » Communication technology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 26 Apr 2026 - 23
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networks, computer science or equivalent, with a strong background on theoretical and applied ML/AI. Your course of study must correspond to a five-year Norwegian course, where 120 credits have been obtained