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Field
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modeling and computational workflows Knowledge about machine learning: statistics and deep learning Experience in data analysis, visualization and presentation Good programming skills in languages such as
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Biological Learning Machine, which is headed by Professor Jan Østergaard. The goal is to develop novel information-theoretic methods for identifying and analyzing temporal and spatial patterns of synergy and
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AI and machine learning applications in health research. Demonstrated ability to manage large datasets and develop predictive models. Excellent written and verbal communication skills. Strong
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academic assignments at the chair What we look for in you Completed master’s degree in computer science, transportation, or related engineering fields Solid background in generative AI, machine learning, and
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qualifications You have graduated at Master’s level in computer science, computer engineering, human-computer Interaction, media technology, visual learning and communication, or closely related fields
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Your Job: In this position, you will be an active part of our "Simulation and Data Lab Applied Machine Learning". Within national and European projects, you will drive the development of cutting
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topics: Software engineering practices for machine learning Tabular machine learning Large language models on structured data PhD Student Role: Under the direction of their supervisor, the candidate will
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frequent cloud contamination. This scale mismatch prevents a coherent representation of radiative–thermal processes at the urban scale. This PhD will develop physics-informed deep learning models for data
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9th November 2025 Languages English Norsk Bokmål English English PhD Scholarship - Deep learning for forest point clouds Apply for this job See advertisement Key Information The position is part of
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is on analysing first-person descriptions of conscious experiences with the help of machine learning and large language models (LLMs) to identify, compare, and systematize different types of states of