-
projects or employment). Your profile You have outstanding experience in Machine Learning with a PhD degree from a university in Computer Science, or related fields, with a proven track record in machine
-
experiments workflows in areas such as simulation, classification, or anomaly detection using machine learning and deep learning methods, exploring, in particular the performance of Quantum Computing
-
-edge topics, and services of high practical relevance. We are seeking someone to join our research team with immediate effect or by mutual agreement: PhD Candidate Distributed Information Systems in
-
characterisation of diagnostic and therapeutic tracers Tumour imaging, clinical oncology and data analysis Semiconductor detectors, nuclear physics and Monte Carlo simulation Machine learning, deep learning and
-
). The PhD dissertation project will focus on how to experimentally capture hypothetical ductus (stroke order and direction of writing) reconstructed by experts and to design deep learning pipelines as
-
stakeholders, and will contribute to the successful completion of the above-mentioned scope. List of the main tasks: Acquire a deep understanding of the scope of the project and of CERN's electrical network
-
Skills and/or knowledge A highly motivated Software Engineer eager to learn and grow in a collaborative and supportive environment; Proficiency in Python programming language, with a solid understanding of
-
is a plus Requirements Additional Information Website for additional job details https://www.hipeac.net/jobs/14573/phd-position-on-secure-machine-learning-on-ri… Work Location(s) Number of offers
-
pollution research Skills in a relevant programming language (e.g., R, Python) are essential, ideally including machine/deep learning and natural language processing Practical experience or good familiarity
-
, telecommunication infrastructure, and high-performance computing. In this context, the Digital Circuits and Systems Group invites applications for one PhD position focusing on the deployment and optimization