We are looking for a talented and motivated Postdoctoral Research Fellow to join our research team at the Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE). The researcher will focus on applying machine learning (ML) techniques to Knowledge Graphs (KGs) for advancing projects in chemical safety, toxicology, and drug discovery. This position is ideal for a candidate with expertise in knowledge graphs, graph embeddings, and ML techniques such as link prediction, neural networks, and recommendation systems. The applied position located in Tampere University will be filled fixed term for 2 years.
The main tasks are:
- Lead tasks in EU projects
- Build Knowledge graphs (data collection, data extraction, data curation)
- Development of machine learning methodologies for learning tasks on knowledge graphs (e.g., graph embedding, link prediction, and node classification).
- Collaborate with interdisciplinary teams, including biologists, chemists, and computational scientists, to tailor ML solutions to specific project needs.
- Publish high-impact papers and contribute to the dissemination of research findings through presentations and workshops.
- Student supervision
Requirements:
- Applicable doctoral degree in a field such computer science, data science, artificial intelligence, bioinformatics or a related field with a strong computational focus.
- Data Integration and Management: Experience working with large, heterogeneous datasets and integrating them into knowledge graphs.
- Knowledge Graph Expertise: Strong experience with knowledge graphs, including their construction, querying, and management (e.g. Neo4j and Cypher)
- Graph Embeddings and Link Prediction: Hands-on experience in generating graph embeddings (e.g., Node2Vec, TransE, etc) and performing link prediction tasks.
- Machine Learning and Neural Networks: Proficiency in ML techniques, including graph neural networks (GNNs), and frameworks like TensorFlow, PyTorch, or scikit-learn.
- Experience with extracting knowledge from text with NLP methodologies
- Programming Skills: Proficiency in Python, with experience in graph libraries such as NetworkX, DGL, PyTorch Geometric, or Neo4j.
- Ability to work independently and within an interdisciplinary research team
- Fluency in spoken and written English.
- Excellent teamwork and interpersonal skills.
We also appreciate:
- Familiarity with toxicological and pharmacological datasets (e.g., OMICs, adverse outcome pathways).
- Experience with semantic technologies (e.g., RDF, OWL, or ontology development).
- Background in deploying machine learning pipelines and APIs for real-world applications.
More information about the position is available on this link.
Applications are open until March 28th, 2025 at 23.59 (EET) and can be submitted here.
For more information, please contact:
Professor Dario Greco, dario.greco@tuni.fi
University Lecturer Angela Serra, angela.serra@tuni.fi
Postdoctoral Research Fellow Michele Fratello, michele.fratello@tuni.fi