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AI/ML Engineer
Accelerate drug discovery and vaccine development by leveraging AI to interpret complex genetic datasets and understand the fundamental language of human cells.
Benefits:
Competitive base salary
Annual bonus based on company performance
Flexible working options available for most roles
Learning and career development
Access to healthcare & wellbeing programmes
Education Requirements:
A minimum of a master’s degree in computer science, applied math, statistics, physics, systems biology, computational biology, bioinformatics, or related field
A PhD in computer science, applied math, statistics, physics, systems biology, computational biology, bioinformatics, or related field
Experience Requirements:
Proven experience in machine learning engineering
Extensive experience in Python programming and knowledge in machine learning and statistics
Demonstrated proficiency working with clinical trial data in an academic or professional setting
Familiarity with at least one Deep Learning framework such as TensorFlow, Keras or PyTorch
Proven ability to solve complex problems using creative approaches, state-of-the-art tools, and best engineering practices
Other Requirements:
Ability to work both autonomously and collaboratively on complex projects
Academic or industry experience in the biomedical sciences especially in respiratory diseases
Prior experience working in clinical trials
Experience with real world evidence
Expertise developing machine learning models within the PyTorch framework
Responsibilities:
Design and implement novel scientific approaches to uncover and explain key relationships within a multitude of biological data types.
Leverage data and insights to produce robust, explainable, and accurate predictions across a variety of key biological and clinical tasks.
Connect and collaborate with subject matter experts in biology, genomics, and medicine.
Identify opportunities to apply the latest advancements in Machine Learning and Artificial Intelligence to build, test, and validate predictive models.
Develop and embed automated processes for predictive model validation, deployment, and implementation.
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