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devices Develop hardware-aware machine learning models incorporating electronic and optical device constraints Design and implement hardware-efficient training methodologies for machine learning systems
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Description Job Summary: The Segel lab at Harvard’s Department of Stem Cell and Regenerative Biology (https://hscrb.harvard.edu/labs/segel-lab/ ) explores the biology of aging through the lens of developmental
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development is important to us – we support you specifically and individually e.g., through training and networking opportunities specifically for doctoral candidates (JuDocS): https://go.fzj.de/JuDocs SUPPORT
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for science, creativity and development. Therefore, we welcome applications from people with diverse backgrounds and perspectives. URL to this page https://web103.reachmee.com/ext/I017/1114/main?site=7
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 1 month ago
for Research Studentships and Fellowships, available on https://dre.pt/application/file/a/127230968 . Workplace: The work will be developed at CiTUA of DECivil / IST, under the scientific supervision of Prof
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Apply online now: https://karriere.klinikum.uni-heidelberg.de/index.php?ac=application&jobad_id=27222 PhD Student – Computational Multi-Omics for Precision Oncology (m/f/d) Stellenanzeige merken
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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this role, you will be involved in the planning, management and delivery of a fast-paced 8-month project on water resources. You will lead the design, development and execution of a multi-model simulation
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d'Arqueologia Clàssica (ICAC) is a public research institution created by the Generalitat de Catalunya and the Universitat Rovira i Virgili. ICAC aims to research, train and disseminate the protohistoric
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics