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at the single-cell level, using tools from optimal transport, mathematical optimization, and machine learning. In addition to method development, the work includes applying and benchmarking algorithms on both
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includes participating in research projects and third cycle courses. The work duties will also include teaching and other departmental duties (no more than 20%). Your research focus will be machine learning
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than 40 PhD students and postdocs. Research at the DSAI ranges from foundational methods in machine learning (e.g., optimization, bandits and reinforcement learning) to application domains in biophysics
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. Documented experience or interest in Artificial Intelligence and Machine Learning development. Proficiency in written and oral communication in English. Place of employment: Karlskrona. Employment level: 100
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given to the ability to assimilate third-cycle courses and study programmes at a higher education. The applicant should have documented knowledge in energy systems and machine learning technologies
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) biological knowledge about GRNs from bioinformatics and system biology, (b) graph theory and topological data analysis for network modeling from mathematics, and (c) robust machine learning (ML) and GenAI from
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biology. The applicant should also have an interest in learning, or previous experience in, computer programming, particularly using languages such as Python. The ideal candidate is driven and a creative
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application! Work assignments Subject area: Computational studies of the influence of microstructural features on the structural integrity of metallic materials using machine learning Subject area description
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highly recognized research. More information about us, please visit: The Department of Biochemistry and Biophysics . Project description The successful candidate will develop machine learning (ML
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to the application deadline. PhD in computer science, electrical engineering, biomedical engineering, or a related field. Experience in Python programming, natural language processing, and multimodal deep learning