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Qubit Pharmaceuticals is looking for an Intern (M/F) to support the project for developing a novel molecular encoding that could outperform the SMILES.
The perfect fit for this job
We are looking for a candidate to participate in a collaborative project at the interface between Machine Learning applied to Drug Discovery and Quantum Computing, to develop a new molecular structure encoding technique with a prospective application that could take advantage of the quantum computing capability.
The candidate will be supervised by experts in the field of molecular modeling, ML for Drug Discovery and Quantum Computing, interested in finding quantum utility in near-term quantum devices.
We are looking for a person that appreciates collaboration in a multi-disciplinary environment, communication, and succeeding with others.
Your role
Investigate in the first phase a novel graph-based representation of molecular systems suitable for predictive modeling in Drug Discovery (DD) applications potentially improving on the state-of-the-art.
Collect evidence for the advantage of the proposed 2D/3D representation running extensive benchmarks against state-of-the-art Machine Learning models.
Exploratory investigation for an advantageous implementation of the new encoding in quantum algorithms, with proven speed-up over classical.
Your Qualifications
Master’s degree in Applied Mathematics, Bioinformatics, Computer Science, Chemoinformatics or Data Science
Solid knowledge and understanding of Statistics and hypothesis testing
Experience in predictive modeling and/or generative modeling of graph-based objects
Advanced python programming
Experience with domain specific python packages (Pytorch, numpy, sklearn, scipy)
Excellent interpersonal and communication skills
Sense of organization, priorities and service
Good written and oral communication skills in English
Optional Qualifications that will increase your success
Basic knowledge in quantum computing
Evidence of academic training in chemistry, structural biology or physics.
Apply now
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