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of small cryptic plasmids in the development and spread of antibiotic resistance, and ii) Use machine learning tools to examine the complex interplay between bacterial hosts, various plasmids and resistance
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bioinformatics, with a particular emphasis on performing analysis of high-dimensional data, which can be sequencing and/or imaging-based. Experience working with AI and machine learning approaches are considered a
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with other disciplines for developing deeper insight into the physical behavior of materials and structures, such as through combinations of finite element methods with machine learning. Tasks also
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, machine learning, molecular biology, or a related field is required. If not yet completed, please provide the expected completion date. A computational background and the ability to code independently in
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computational biology with a focus on developing new and scalable computational models (e.g. deep learning, machine learning, optimization or statistics) or integrating data-driven applications to address
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humans and society at large is either fully automated or heavily relies on automatically provided decision support. While machine learning approaches become increasingly prevalent in this context
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or more of the following programming languages/environments: Unity/Unreal, C/C++, C#, Python Basic understanding of the artificial intelligence and machine learning fields. Place of employment: Karlskrona
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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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in computer science, engineering, data sciences, applied mathematics, machine learning, or another related field; or Have completed at least 240 credits in higher education, with at least 60 credits at
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and information science, interaction design, or has equivalent scientific competence. Equivalent scientific competence generally refers to a degree from a foreign university with essentially the same