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Field
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excellent study records in Electrical Engineering, Electronics, Microelectronics, Computer Engineering, or a closely related field, completed by the start of the position Have a solid background in digital
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1st August 2026 Languages English English English The Department of Energy and Process Engineering has a vacancy for a PhD Candidate in Plastics Pollution Modelling and Life Cycle Assessment Apply
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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High
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related policy/business discipline. Have familiarity with empirical research methods, experimental design, or technical modelling relevant to your field. Having a demonstrated publication record or
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analyses or game theoretic analysis. Experience with large language models, machine learning, and/or programming in R or equivalent programs is an advantage but not a requirement. The evaluation of
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microfluidic fabrication and experiments 3D printing machine learning. Demonstrated programming skills (Matlab, C++, or Python). Desired Demonstrated ability to work independently and to formulate and tackle
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research experience in e-health, digital health or a related field experience of, or a documented interest in, machine learning, AI methods or large language models (LLMs) in clinical or health-related
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software or similar languages and experience with modern machine learning and deep learning frameworks parallel computing using clusters like UPPMAX and GPUs for high-performance computing and parallel
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Development Design new statistical and machine learning models tailored to this emerging omics modality. Multimodal Data Analysis Work with high-dimensional datasets combining quantitative RNA features
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the integration of modern machine learning tools into computational catalysis workflows. Write research proposals for funding and manuscripts for publication. Assist the PI with the supervision of students and