Biomedical and Health AI Scientist (KTP Associate) in London

• Biomedical and Health AI Scientist (KTP Associate)
• Developing an AI-enabled biomarker discovery engine, using multi-modal data, to address growing demand in the precision medicine market for disease risk prediction services
• Handling and analysing biomedical signals, medical text and image data
• Accessing, managing and handling new to Hurdle data types such as EHRs and wearable signals
• Fusing these new data types with multi-omics data
• Finding novel ways of bringing a temporal dimension to Hurdle’s predictive models using state-of-the-art time series analysis methodologies
• Finding ways to reduce the cost of data acquisition and speed of discovery — model performance will need to be carefully balanced with commercial drivers
• Contributing to embedding expert knowledge into Hurdle’s staff base
• Contributing to academic papers

These duties are a guide to the work that the post holder will initially be required to undertake. They may be changed from time to time to meet changing circumstances.

Key requirements

Qualifications

• A higher degree (Master’s) in computer science, data science or AI, or bioinformatics

Experience/Knowledge

• Experience of deploying state-of-the-art AI and ML techniques in an industry setting
• Experience of working with health-related data, particularly medical images and timeseries and biosignal processing
• Experience with data preprocessing, feature engineering, and statistical analysis in healthcare settings
• Experience of creating demos and prototypes utilizing models
• Experience of producing and presenting data visualisations
• Knowledge of cloud systems (AWS and GCP)
• Experience of running, training, and evaluating a variety of ML models
• Experience of working with different biomedical signals, such as medical images and timeseries
• Experience working with large-scale cohorts (e. g., UK Biobank, Our Future Health, All of Us) and handling population-level biomedical data
• Experience with multimodal biomedical data, including MRI, CT, pathology imaging, and UK Biobank imaging modalities (MRI, DXA, fundus, OCT, ultrasound)
• Exposure to multi-omics datasets (genomics, proteomics, transcriptomics, metabolomics)

Skills/Abilities

• Advanced statistical and technical skills for working with large databases
• Higher level programming language skills in Python
• Skills with technologies used for data storage, access and manipulation
• High-level skills in data fusion and time series techniques
• Knowledge of fine-tuning open-source models
• Excellent communication and presentation skills
• Excellent command of written and spoken English
• Ability to work as an effective team member and independently as and when required
• Ability to manage a budget and forecast expenditure
• Familiarity with privacy-preserving ML techniques (e. g., federated learning, differential privacy)

As a KTP Associate, the post will offer the following benefits

• A personal development budget of £5,340 (exclusive of salary).
• Management training and mentoring by an Innovate UK KTP Adviser.
• An interesting and challenging role, with exposure to a variety of stakeholders.
• Full access to university resources to complete the project.
• World-leading Academic and Company project supervision, with project support by a dedicated, sector leading KTP Office.

The University of Essex, in partnership with Hurdle, offers an exciting opportunity to a graduate with the relevant skills and knowledge to develop an AI-enabled biomarker discovery engine, using multi-modal data, to address growing demand in the precision medicine market for disease risk prediction services.

About the business

Founded in Cambridge in 2017, Hurdle is a technology scale-up company providing the infrastructure, technology, and expertise to enable the development, validation, and deployment of multi-modal diagnostics at scale.

Supporting partners across Pharma, Biotech, Healthcare, and Diagnostics, Hurdle offers end-to-end capabilities, from biomarker discovery to regulatory-ready implementation across research and clinical settings.

Its platform integrates molecular (e. g. epigenomic, genomic, transcriptomic), clinical, digital, and real-world data using AI-driven analytics and software automation.

This infrastructure enables the generation of insights that inform surrogate endpoints, guide treatment decisions, and support precision stratification.

By standardizing diagnostic development, Hurdle helps accelerate the delivery of fit-for-purpose tools that advance precision medicine.

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