InstaDeep, founded in 2014, is a pioneering AI company at the forefront of innovation. With strategic offices in major cities worldwide, including London, Paris, Berlin, Tunis, Kigali, Cape Town, Boston, and San Francisco, InstaDeep collaborates with giants like Google DeepMind and prestigious educational institutions like MIT, Stanford, Oxford, UCL, and Imperial College London. We are a Google Cloud Partner and a select NVIDIA Elite Service Delivery Partner. We have been listed among notable players in AI, fast-growing companies, and Europe's 1000 fastest-growing companies in 2022 by Statista and the Financial Times. Our recent acquisition by BioNTech has further solidified our commitment to leading the industry.
Join us to be a part of the AI revolution!
Role Description:
We’re looking for a candidate to contribute to the development of state-of-the-art machine learned interatomic potentials (MLIPs) for materials and molecular modelling/simulations.
As a PhD Intern in the London Research Team you will be responsible for
implementing and developing active learning strategies for fine-tuning ML-driven atomistic models. This involves identifying and investigating promising research directions related to efficient data acquisition, model uncertainty, and generalisation across chemical systems.
Recent advances in machine learning, including pre-trained models and automated data selection techniques, offer exciting opportunities for adaptive simulation pipelines in materials discovery. However, many open challenges remain on how to best integrate active learning with atomistic simulations at scale. Your work will help address these challenges, combining rigorous experimentation with novel algorithmic insights.
Role Responsibilities
Support the efforts of the Research Team through the development of novel methods and applications under the guidance of our Research Scientists and Engineers.
Design and implement data acquisition strategies for proof of concept and finetuning MLIP models.
Write high-quality, maintainable, well-documented, and modular python code.
Report and present experimental results and research findings, verbally and in writing.
Contribute to team research and publications.
Requirements
Currently enrolled in a PhD programme (or recent graduate) in a related STEM discipline.
Experience coding with deep learning frameworks such as JAX, Pytorch and/or Tensorflow, along with theoretical and practical knowledge in machine learning, deep learning, and Bayesian optimization.
Excellent communication skills and collaborative spirit.
Good programming skills in Python.
Proven ability to contribute to research communities and/or efforts, as evidenced by publishing scientific papers in leading journals or conferences (JMLR, ICLR, NeurIPS, ICML, etc.).
Work permit for the UK for the duration of the internship. We don't sponsor Visas.
Specific experience in any of the following domains: coding in JAX, running MD simulations, and/or running DFT calculations.
Experience building machine learning models for quantum chemistry, and training within an active learning setting.
Our commitment to our people
We empower individuals to celebrate their uniqueness here at InstaDeep. Our team comes from all walks of life, and we’re proud to continue encouraging and supporting applicants from underrepresented groups across the globe. Our commitment to creating an authentic environment comes from our ability to learn and grow from our diversity, and how better to experience this than by joining our team? We operate on a hybrid work model with guidance to work at the office 3 days per week to encourage close collaboration and innovation. We are continuing to review the situation with the well-being of InstaDeepers at the forefront of our minds.
Right to work: Please note that you will require the legal right to work without visa sponsorship in the location you are applying for. We do not sponsor work visas.
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