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data analysis, while contributing to fundamental questions in evolutionary biology. We welcome applicants with training in biology or related quantitative fields (mathematics/physics/computer science
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large language models (LLMs)—that is, the inability of a model to effectively process or understand visual information. This work involves integrating visual encoders with language models to create
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higher education credits in the subjects of physics, computer science, mathematics, mathematical statistics, or related areas, of which at least 30 credits must be at the advanced (master's) level. Courses
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)—that is, the inability of a model to effectively process or understand visual information. This work involves integrating visual encoders with language models to create multimodal systems. The emphasis is
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Computational Physics, the applicant must have completed at least 90 higher education credits in the subjects of physics, computer science, mathematics, mathematical statistics, or related areas, of which
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corresponding knowledge in another way. A successful candidate should have excellent study results and a strong background in mathematics. The applicant should be skilled at implementing new models and algorithms
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format. This will allow combinations of neural networks with physics models. The project brings together PhD students and senior researchers from multiple disciplines to tackle challenges in sustainable
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applicant must have passed courses within the first and second cycles of at least 90 credits in either, a) Chemistry/Molecular Biology/Biotechnology, or b) Computer Science/Mathematics/Physics and at
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) Computer Science/ Mathematics/Physics and at the second cycle level, 60 credits in Life Science, Computer Science Mathematics, Physics or Bioinformatics including a 30 credit Degree Project (thesis). Additional
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conditions The doctoral student will be employed on a doctoral studentship maximum 4 years full-time. Application process Submit your application and supporting documents through the Varbi recruitment system