Sort by
Refine Your Search
-
Listed
-
Program
-
Field
-
) Focus on Microbiome Data Science and Explainable Machine Learning Core research themes We are looking for motivated and skilled students to join our research team in the field of plant microbiome data
-
experimental data. Develop computational frameworks for integrating spatial and bulk multi-omics datasets. Create and apply statistical and machine learning models for feature extraction, data harmonisation, and
-
related field Solid understanding of machine learning, especially deep learning and transformer models Practical experience with Python and ML frameworks (e.g., PyTorch, HuggingFace, NumPy, sklearn) Basic
-
. The positions focus on applied machine learning methods for real-world systems. Possible research directions include: Transfer learning and domain adaptation across heterogeneous production environments (e.g
-
strong foundations in machine learning and artificial intelligence, as well as a solid mathematical background. The position requires a strong interest in exploring multiple research directions toward
-
accessibility by public transport or car (including free parking) 30 days of vacation Participation in the benefits program for employees („Corporate Benefits“) The BIO-MICRO project Please find a description of
-
information retrieval, data integration, machine learning/AI, LLMs, knowledge graphs excited to use vector databases, e.g. integrating deepset haystack for RAG interested in experimenting with solr, postgres
-
Pandemic Disease in Preindustrial Europe (1300–1800): Combining History, Machine Learning, and the Natural Sciences (EUROpest)”, funded by the European Research Council Executive Agency (ERC) as an ERC
-
: active learning (uncertain cases first), smart sampling, confidence thresholds, gradations (auto-label/review/manual), measurement and decision logic for throughput vs. quality. Proficiency in programming
-
Pandemic Disease in Preindustrial Europe (1300–1800): Combining History, Machine Learning, and the Natural Sciences (EUROpest)”, funded by the European Research Council (ERC) as an ERC Synergy Grant