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Research Scientist
Ellison Institute of Technology Oxford. Conceptualize and develop original evaluations, training algorithms and fine-tuning recipes, connecting them to ongoing design-build-test loops .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing innovative methodologies and simulation-based pipelines, with a strong focus on applying machine learning to accelerate experimental outcomes. Proven ability to mentor and collaborate with cross-disciplinary teams while producing high-impact research publications.
Highest-signal resume keywords
PhD In Machine LearningDeep Learning Frameworks ExpertiseSimulation-Based Pipeline DevelopmentHigh-Quality Research Code WritingEnd-To-End Machine Learning Pipeline Validation
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningDeep LearningBoltzmann SamplersMolecular DynamicsMachine-Learned Interatomic PotentialsFlow MatchingDiffusion ModelsPredictive Simulation WorkflowsResearch Code DevelopmentExperimental Campaign Evaluation
Soft Skills
MentoringCollaborationDesire For Real-World Problem SolvingHigh Velocity Execution
Industry Keywords
NeurIPSICMLICLRQuantitative FieldAI VenuesImpactful ResearchExperimentalistsScientific Domain ExpertsData Domain ExpertsInfrastructure Teams
About the role
Key responsibilities & impact- Conceptualize and develop original evaluations, training algorithms and fine-tuning recipes, connecting them to ongoing design-build-test loops
- Translate research work to experimental campaigns with a rapid feedback cycle
- Mentor PhD students
- Co-author high-impact publications, including NeurIPS, ICML and ICLR papers, and technical reports
- Partner with cross-disciplinary squads of experimentalists, scientific domain experts, data domain experts and infrastructure teams
- Develop innovative methodologies such as Boltzmann samplers, long-timestep molecular dynamics, machine-learned interatomic potentials, flow matching and diffusion models
- Conduct full-stack research from pre-training and fine-tuning to design-build-test loops
- Develop predictive simulation-based workflows accelerated by machine learning
- Merge physics and machine learning for robust generalization
- Evaluate research innovations based on measurable acceleration of experimental campaigns
Requirements
What you’ll need- PhD in Machine Learning, AI, Computer Science, or a related quantitative field (Physics, Math, Computational Biology), or equivalent experience
- Proven track record of impactful research, demonstrated by publications in top-tier AI venues or scientific journals
- Deep expertise in deep learning frameworks
- Ability to write high-quality, reproducible research code
- Strong desire to apply advanced research to tangible, real-world problems in a fast-paced environment
- Expertise building simulation-based pipelines to predict experimental outcomes
- Experience using machine learning to accelerate simulation-based pipelines
- Experience with production grade pipelines for small molecules, biomolecules design and simulations
- Agentic engineering skills
- Ability to build and validate end-to-end machine learning pipelines and in-silico design-build-test loops
- Ability to ship at a high velocity
Benefits
Comp & perks- Enhanced holiday pay
- Pension
- Life Assurance
- Income Protection
- Private Medical Insurance
- Hospital Cash Plan
- Therapy Services
- Perk Box
- Electric Car Scheme
- Collaborative, inclusive and supportive work environment
- Creative risks encouraged
- Commitment to excellence