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Field
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representation learning Design reproducible data and experimental workflows for model training, adaptation and evaluation on selected EO applications Investigate MLOps pipelines for reproducible, efficient and
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examines whether individual learning becomes part of shared organisational practice. DC08: Unlearning Obsolete Rules: Designing for Sunset and Regulatory Delearning Host university: BI Norwegian Business
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Constructor Technology, invites applications for a PhD position in machine learning for software engineering and formal methods, on the Constructor Fabric project. Constructor Fabric turns a company's informal
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Max-Planck-Institut für Kohlenforschung, Mülheim an der Ruhr | M lheim an der Ruhr, Nordrhein Westfalen | Germany | 3 days ago
combines experiments, quantum chemistry, and spectroscopy to study reaction mechanisms and to discover how solvation and catalyst design affect the rate and selectivity. Current projects focus on developing
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on futuristic technologies in the field of machine learning and computer vision. Hence, we investigate and develop an innovative computation-in-memory (CIM) solution for artificial intelligence accelerator design
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PhD Position - Operando X-ray Characterization of Catalytic Interfaces for Chemical Hydrogen Storage
into rational design principles for high-performance and durable catalysts. Plan and conduct research on the catalytic conversion of chemical hydrogen carriers, with scientific guidance from the supervisory team
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Max Planck Institute of Colloids and Interfaces | Potsdam, Brandenburg | Germany | about 2 months ago
) for Functional Material Design in Summer 2027! We are recruiting doctoral candidates to help shape a resource-efficient future. The candidates will work on individual projects exploring how we can learn from
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application. The project aims to change this by realizing pulsed DNP on a chip. What you would be doing PhD 1: • explore architectures and design the pulsed-DNP chip • oversee chip manufacturing and
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longitudinal within-person models, discrete-time survival analysis, and/or explainable machine learning. Each of the three work packages is designed to result in one publication, together constituting a
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and (2) develop learning rules that are both technology-feasible and well-suited for machine-learning workloads. The project will consist among others of the following tasks: Investigate and design