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/id_offre/136106 Requirements Specific Requirements Engineer and / or Master of Science degree (candidates may apply prior to obtaining their master's degree. The PhD will start though after the degree is
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teaching duties. Requirements: Applicants should possess a PhD degree in Computer Science, Computer Engineering, Information Systems, or a related field, and sufficiently demonstrate abilities to conduct
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experience in electrochemistry, or environmental engineering and water technology. Experience with multivariate data analysis, statistics, machine learning, numerical simulations, and programming. Skills in
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network management. You will have a good first degree in physics, electrical and electronic engineering, computer engineering/science, or another relevant subject, and a PhD or equivalent experience in
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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the DFG Priority Programme “Molecular Machine Learning” and embedded in the research project “Multi-fidelity, active learning strategies for exciton transfer in cryptophyte antenna complexes”. The PhD
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problems, statistical learning and machine learning (machine learning, deep learning) - Knowledge of associated software development tools and environments: Python, PyTorch, Scikit-learn, Jax, Julia
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Brain Decoding Models across Subjects School of Electrical and Electronic Engineering PhD Research Project Self Funded Dr Mahnaz Arvaneh Application Deadline: Applications accepted all year round
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dissemination, and translational opportunities Job Requirements: PhD in Chemistry with a focus on computational/peptide/organic/machine learning or a closely related discipline At least one first-author
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Chemical Engineering, Materials Science, or a related field The candidate should have a background in at least one of the following areas: quantum mechanics, molecular simulation, and machine learning