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analysis and processing advantageous Interest in applying artificial intelligence and machine learning techniques advantageous Grade requirements: The average grade point for courses included in
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. The work will be immersed in a multidisciplinary team and involve close collaboration between molecular biologists, neuroscientists and machine learning experts in our research group as well as with
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background in computational science. Experience with implementation or applications of large machine learning models, transformers or text embeddings is considered an advantage. Interest for interdisciplinary
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educational system is required The candidate must have interest and experience in linguistic aspects of DPPs, language classification techniques, machine learning, collecting and analyzing linguistic data, and
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collaboration between molecular biologists, neuroscientists and machine learning experts in our research group as well as with collaborators at e.g. Harvard University and University of California San Diego
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Experience with high-throughput sequencing omics data analysis Proficiency in programming with Python, R, or C++ Desired: Familiarity with statistical and machine learning techniques. Knowledge about molecular
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-throughput screening of novel electrolytes for solid-state sodium ion batteries by means of machine learning (ML) techniques and density functional theory (DFT) simulations as well as experimental validation
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with implementation or applications of large machine learning models, transformers or text embeddings is considered an advantage. Interest for interdisciplinary research Candidates without a Master’s
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classification techniques, machine learning, collecting and analyzing linguistic data, and creating multilingual corpora The candidate should have relevant publications on linguistic aspects of DPPs The candidate
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ion batteries by means of machine learning (ML) techniques and density functional theory (DFT) simulations as well as experimental validation. The PhD Research Fellow will mainly be involved in tasks