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Field
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Experience Appropriate PhD in a related field. Preferred Qualifications Experience with machine learning and deep neural network techniques. Experience with wearable and sensors placed in the environment
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The applicant must: hold a PhD in a relevant field (e.g. computer science, artificial intelligence, machine learning, computer vision, animal science, biology, veterinary medicine, or a related discipline) have
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++, or JavaScript Experience with AI or machine learning techniques, including large language models or agentic systems Experience developing or integrating interactive visualization systems, including web-based
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Max Planck Institute for Extraterrestrial Physics, Garching | Garching an der Alz, Bayern | Germany | 25 days ago
Studies (CAS ) at the MPE invites applications for an Astrochemistry Postdoctoral on computational chemistry. A particular focus of the project is on utilizing machine learning techniques for molecular
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École nationale des ponts et chaussées | Champs sur Marne, le de France | France | about 1 month ago
of the project, and will contribute to shaping the scientific directions of the AUTOMATIX project. Context The increasing availability of full-field experimental data and advances in machine learning offer new
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, leadership, and data science. Special training for writing successful ERC Starting Grants as a ‘ticket’ to an outstanding academic career. Being part of a thriving academic and social community in Vienna, one
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research project Expertise in machine learning Additional expertise in one or more of the following: digital signal processing, statistics, multimodal processing, FAIR data management, music theory
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data, identifying structural errors in the dataset, and for maintaining a record of all steps from data extraction to dataset assembly · Fitting of machine learning models · Development of instrumental
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for genomics (e.g., generative models, transformers, agentic workflows) and/or statistical learning (e.g., network & spatiotemporal modeling, functional/longitudinal data, time-series). Analyze single-cell
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comprehensive support for large‑scale multi‑omic data generation/analysis and transformation/embryogenesis services for functional validation. Nathaniel is also an associate group leader at the Science for Life