PostDoc Researcher - Graph Representation Learning and Explainable AI
About Accenture Labs Dublin
Accenture Labs is a network of R&D labs distributed on seven locations worldwide, home of over 200 applied R&D experts. The Dublin Labs team focuses on artificial intelligence, with a strong emphasis on explainable AI, machine learning on knowledge graphs (graph representation learning), and computational creativity. The lab is co-located with the over 100 designers, developers and domain experts at The Dock, Accenture's newly-created global centre for innovation.
We offer a blend of industrial-related applicative problems and academic-oriented activities, including an open publication policy.
We are looking for one PostDoc researcher that will join a newly-created international research consortium. The length of the PostDoc is 3 years. The successful candidate will join a multi-partner project whose goal is identifying factors that can cause development of new medical conditions, and worsen the quality of life of cancer survivors. The project will analyse patient’s clinical, genomic, behavioural data and existing open data in order to determine a follow-up adapted to the individual needs.
The successful candidate will join our team of AI researchers and engineers, and work on the aforementioned applicative research activities focused on explainable AI and machine learning on knowledge graphs. The candidate will be in charge of designing, developing, evaluating, and applying novel models. That will include software development (including contributing to our open source stack), carrying out experiments, and publishing results in academic venues.
More precisely, the candidate role will focus on designing interpretable machine learning models to infer knowledge from a knowledge graph that models the aforementioned data. Explanations will rely on a wide range of AI techniques, such as rules derived from the inherent deep learning models or graph-based explanations derived from the input data by network analysis models and interpretable machine learning approaches. Predictions and explanations will employ rich background knowledge on cancer biology.
- PhD in computer science, statistics, mathematics or related field.
- Proven communication skills (talks, presentations, academic publications)
- Strong foundation in mathematics, statistics and probability
- Strong knowledge of Machine Learning foundations
- Knowledge of mainstream Deep Learning architectures
- Ability to work creatively and analytically in a problem-solving environment
- Strong Python programming skills
- Hands-on experience with machine learning frameworks e.g. Scikit-learn, TensorFlow, PyTorch, and scientific Python (e.g. numpy)
- Eagerness to contribute in a team-oriented environment
• Publications in flagship conferences such as NeurIPS, IJCAI, ICLR, AAAI, ICML, KDD, The Web Conf, ISWC
• Experience with graph-based knowledge representation (e.g. knowledge graphs)
• Familiarity with graph representation learning (e.g. knowledge graph embeddings, graph neural networks)
• Experience with explainable AI or interpretable machine learning techniques
• Working knowledge of Linux OS and shell scripting
• Hands-on experience with git and issue tracking systems
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