PhD Research Engineer Intern - Active Learning for Molecular Modelling
London
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InstaDeep
InstaDeep delivers AI-powered decision-making systems for the Enterprise. With expertise in both machine intelligence research and concrete business deployments, we provide a competitive advantage to our customers in an AI-first world.
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.
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.
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.
Beneficial
- Software engineering skills (git, quality coding standards, …)
- Experience implementing/training/fine-tuning molecular models.
- 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.
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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Categories:
Engineering Jobs
Machine Learning Jobs
Research Jobs
Tags: Bayesian Chemistry Deep Learning Engineering GCP Git Google Cloud ICLR ICML JAX JMLR Machine Learning ML models NeurIPS PhD Pipelines Python PyTorch Research STEM TensorFlow
Perks/benefits: Career development Conferences
Region:
Europe
Country:
United Kingdom
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