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Field
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Science, Machine Learning, Computational Linguistics or a related field, if applicable with PhD previous experience in Natural Language Processing, knowledge Graphs, Machine Learning or Recommender Systems
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documents uploaded using the dedicated electronic form. helpdesk: petra.koudelova@fsv.cvut.cz Machine learning for 3D printed multifunctional metamaterials Description: The PhD topic focuses on the use
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transferability; (ii) sample-inefficient learning requires large volumes of annotated, domain-specific data; and (iii) complex architectures with tightly coupled components hinder modular adaptation. To address
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, technical depth, and a strong track record of applied research in Computational Biology, Structural Biology, Protein Engineering, Machine Learning, or a closely related field. Strong understanding and
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structural mechanics, Structural Health Monitoring (SHM), Non-Destructive Testing (NDT), or related areas • Experience in data-driven methods for engineering applications (e.g., machine learning, prognostics
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Machine Learning Lab (AMLab) conducts research in machine learning, artificial intelligence, and its applications to large scale data domains in science and industry. This includes the development of deep
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the undergraduate and graduate levels. Where to apply Website https://www.bth.se/english/vacancies/job/phd-student-position-in-software-engin… Requirements Research FieldComputer science » Computer systemsEducation
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responsibilities? No Preferred Education/Experience Bachelor’s degree in Computer Science, Machine Learning, AI, Data Science, Engineering, Mathematics, or related field (Master’s or PhD preferred). 7+ years
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Social Science / Machine Learning / Data Science would be a plus Experience of organising and conducting a variety of quantitative and qualitative research techniques and methods Skills &Competencies
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experience in metaproteomics should clearly demonstrate their motivation and readiness to develop expertise in this field; • Experience in multi-omics integration, machine learning, or microbiome data analysis