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. To do so, you will combine atomistic simulations (density functional theory and ab-initio molecular dynamics simulations) with new machine learning models to parameterize machine learning force fields
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digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design
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qualifications include: Ph.D. in Computer Science, Computer Engineering, Electrical Engineering or a related field; Strong background in Deep Learning (e.g., Transformers, foundation models); Strong programming
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collaborate with experts in machine learning, immunology and microbiology. You are expected to work independently and coordinate your research with the other team members. Undergraduate research projects will
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in Computer Science, Machine Learning, Artificial Intelligence, Computational Biology, or a closely related field Has strong theoretical and practical experience in deep learning Has hands
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in electrical engineering, computer engineering, computer science, or similar. Strong background in communication systems, optimization, or machine learning for networked systems. Experience and
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sustainable machine learning approaches and addressing renewable energy related projects. Likewise, deploying a novel paradigm of KGML (knowledge guided machine learning) can propel further research. PhD
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imaging, deep proteomics, metabolomics, metaproteomics, and machine learning (ML) approaches to develop diagnostic classifiers, spatial tissue atlases, and identify potential therapeutic targets
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initiatives Commitment and ability to teach and supervise students at bachelor’s and master’s levels, including course development in digital design, computer architecture, and AI hardware Strong communication
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of machine learning Distributed and federated training The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics