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Artificial Intelligence and Machine Learning development, Proficiency in written and oral communication in English. Place of employment: Karlskrona. Employment level: 100%. Commencement: Early Fall 2026, exact
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Science degree in Computer Engineering. A person who, in some other way, in the country or abroad, has acquired equivalent qualifications also meets the specific entry requirements. To be eligible
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advanced statistical analyses and machine-learning techniques. Work duties and responsibilities The main task for a doctoral student is to devote yourself to your doctoral studies, therefore you will be both
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Machine Learning and Computer Vision Excellent communication skills in English Good knowledge and experience of Python or R programming Experience in implementing public, open-source software Experience in
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Council to establish a center of excellence: Center for modeling adaptive mechanisms in living systems under stress. We are now opening a tenure-track Assistant Professor position in machine learning with
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, driven by high-quality research and education, openness and collaboration. As a Teaching Fellow, you will contribute to this goal through engaging teaching and learning in a collegial and inclusive
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sciences, caring science, medicine, welfare technology, informatics, computer science, artificial intelligence, information systems, medical technology, engineering, statistics, machine learning, human
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. The department is BTH's largest department, with 70 people. The research at the department is conducted in the area of computer science. This includes the subareas of big data and AI, parallel computer systems
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a doctoral student with a strong background in machine learning, mathematics, and modeling, and an interest in biological systems. The successful candidate will join a project to understand and model
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. The department is BTH's largest department, with 70 people. The research at the department is conducted in the area of computer science. This includes the subareas of big data and AI, parallel computer systems