Назад
6 дней назад

Senior Research Investigator Biologics Computational Science (Machine Learning)

Формат работы
hybrid
Тип работы
fulltime
Грейд
senior
Страна
UK
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Описание вакансии

TL;DR
Senior Research Investigator Biologics Computational Science (Machine Learning): Developing computational and machine learning methods for biologics discovery and therapeutic design with an accent on sequence-based property prediction, model benchmarking, and laboratory data analysis. Focus on identifying high-value data for active learning, building models for functional and biophysical properties, and applying computational methods to discover novel therapeutics.

Senior Research Investigator Biologics Computational Science

Company

Isomorphic Labs

Conditions

6 days ago

Skills

Candidate Availability

Required and preferred rules are kept separate and reflect the wording in the original posting.

About the Role

You will develop and apply computational and machine learning methods to support biologics discovery. You will predict therapeutic properties from sequence, benchmark models, analyze laboratory data, identify valuable data to generate, and develop models that predict functional and biophysical properties of biologics.

Requirements

  • Experience using computational and machine learning methods to predict biologics properties
  • Experience developing methods to predict affinity, developability, or immunogenicity from sequence
  • PhD or equivalent in computer science, computational biology, structural biology, biochemistry, or a related field
  • Proficient Python programming skills
  • Experience preparing datasets, benchmarking models, and working with structure-based metrics

Responsibilities

  • Identify computational modelling opportunities for biologics discovery programs
  • Develop computational and machine learning methods to predict biologics properties from sequence
  • Apply computational methods to discover novel therapeutics
  • Benchmark computational methods and develop baseline models
  • Analyze laboratory experiment data to inform biologics discovery decisions
  • Determine high-value data to generate for machine learning models
  • Implement model-aware prioritization for active learning
  • Develop methods to predict functional properties of biologics using biophysical constraints

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