Research Scientist and Data Engineer on Graph Modeling and Machine Learning
vor 2 Monaten
We are seeking a highly motivated researcher and software developer to join our dynamic multidisciplinary team at the German Cancer Research Center (DKFZ) and the National Center for Tumor Diseases Heidelberg. The successful applicant will work on cutting-edge projects and have the opportunity to make a significant impact on data-driven biomedicine.
Your Tasks- Design, develop, and maintain graph database solutions using Python and graph database technologies (e.g. Neo4J, ArangoDB, OrientDB)
- Implement data import/export processes and ETL pipelines for graph data and write efficient and performant code to query and manipulate graph data
- Collaborate with our team to apply graph-based models to biomedical datasets and implement and optimize algorithms for efficient computation on large-scale graphs
- Design and develop graph machine/deep learning algorithms to solve challenging problems in biomedicine
- Stay current with the latest developments in graph machine learning and develop/integrate new techniques into biomedically-relevant research projects
- Conduct testing and provide thorough documentation
Mandatory qualifications:
- A master's or PhD degree in computer science, data science, computational biology, electrical engineering, physics, mathematics, statistics, or related field with a focus on machine learning and graph theory
- Strong foundation in machine learning, deep learning, and graph theory
- Experience with graph neural networks and related libraries, tools, and frameworks (e.g. PyTorch Geometric, Deep Graph Library, GraphSAGE)
- Proficiency in programming languages such as Python, TensorFlow, PyTorch or similar tools for machine/deep learning development
- Strong problem-solving skills, excellent written and communication skills, with the ability to work independently but also within our multidisciplinary research environment
- Ability to collaborate with life scientists, clinicians, and researchers from diverse disciplines to address research questions at the intersection of graph machine learning and biomedicine
Optional qualifications:
- Knowledge of graph database concepts and technologies (e.g. Neo4J, ArangoDB, OrientDB)
- Experience in managing diverse data types, integrating multi-omics datasets, and dealing with data heterogeneity
- Proficiency in data preprocessing, normalization, and feature extraction techniques specific to biomedical applications
- Experience in constructing, visualizing, and analyzing biological networks (e.g. protein-protein interaction networks, gene regulatory networks, metabolic pathways, disease networks)
We offer an excellent framework with state-of-the-art equipment and opportunities for international networking at the highest level. Our team is committed to creating a diverse and inclusive work environment, and we welcome applications from talented individuals from all backgrounds.
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Heidelberg, Baden-Württemberg, Deutschland DKFZ VollzeitResearch Scientist and Data Engineer on Graph Modeling and Machine LearningWe are seeking a highly motivated Research Scientist and Data Engineer to join our team at the German Cancer Research Center (DKFZ). As a key member of our multidisciplinary team, you will be responsible for designing, developing, and maintaining graph database solutions using Python...
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