Snabbfakta
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- London
Ansök senast: 2024-11-30
PhD Research Scientist
Founded in 2014, InstaDeep is today an EMEA leader in decision-making AI products for the Enterprise, with headquarters in London, and offices in Paris, Berlin, Tunis, Kigali, Cape Town and the USA. InstaDeep has been named among the Top 100 global AI startups for three consecutive years by CB Insights, as well as one of the 100 most promising B2B companies in Europe, by Sifted. We have created an environment that ensures a challenge by working closely with a broad spectrum of high-quality clients, pushing you to thrive in an environment that’s rarely tedious and always looking to push you to come up with outside-the-box solutions using cutting-edge technologies. InstaDeep is looking for Research Scientists to join our Research Team in London, working at the intersection of machine learning and life sciences. Our in-house Research Team aims to build foundational expertise and develop the next-generation of ML-driven technologies for life science applications; for example, generative models for protein sequence and structure, and model-guided experimental design and more.
As a Research Scientist, you will be responsible for implementing and developing novel algorithms, as well as identifying and investigating promising research ideas in the field of multi-round experimental design, active learning, Bayesian optimization, model-based reinforcement learning, large language models, representation learning and uncertainty quantification, to relevant challenges in biological sequence design.
You will leverage your expertise to help shape and deliver our research agenda, enabling us to stay ahead of the curve in this exciting field.
Develop and implement novel research on Bayesian experimental design for multi-round optimisation of biological sequences.Contribute to team research and publish results at leading journal and conference venues.Suggest and engage in collaborations to meet Research Team goals. Report and present experimental results and research findings, both internally and externally, verbally and in writing.PhD in Computational Biology, Machine Learning or a related scientific field.Theoretical and practical knowledge of deep learning, LLMs, Bayesian optimization, non-parametrics, model-based RL/planning.A demonstrated ability to successfully deliver high-quality research, for example through the publication of scientific papers in journals or conferences.Software development skills in Python.
a) Experience working with large biological datasets, databases (PDB, Uniprot, etc.) b) Protein language models c) Drug discovery and protein engineeringExperience with any of the following machine learning topics would be a plus. a) Bayesian experimental design, active learning, Bayesian optimization, model-based reinforcement learning, b) Large Language Models c) Generative models
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