Postdoctoral Research Assistant Managing Chemical Uncertainty

ASTRAZENECA UK LIMITED; Macclesfield; On-site; GBP 35,000 - 45,000; Full time

Job summary

A global biopharmaceutical company is seeking a Postdoctoral Research Assistant to lead the development of innovative systems for managing chemical uncertainty in drug development. The role involves tackling challenges using AI and cheminformatics. Candidates should have a PhD in a relevant field, hands-on coding experience, and strong problem-solving skills. This two-year fixed-term position offers a chance to contribute to significant advancements in analytical workflows and regulatory compliance.

Benefits

Competitive salary and benefits

Qualifications

• PhD in Cheminformatics, Computational Chemistry, Data Science, or related disciplines.
• Hands-on coding ability in Python or similar languages.
• Understanding of cheminformatics or analytical chemistry workflows.
• Experience with complex datasets and uncertainty handling.
• Strong problem-solving, teamwork, and communication skills.

Responsibilities

• Develop approaches for representing chemical uncertainty.
• Build AI-driven systems for impurity tracking.
• Create tools for interpreting analytical data.
• Integrate predictive models with analytical data.
• Collaborate across scientific disciplines.
• Drive adoption and share knowledge with stakeholders.
• Contribute to the broader scientific community.

Skills

AI/machine learning development

Cheminformatics

Computational Chemistry

Data Science

Analytical Chemistry

Python coding

Problem-solving

Teamwork

Communication skills

Education

PhD in a relevant field

Job description

Overview

As a Postdoctoral Research Assistant, you will lead the development of an innovative system for managing chemical uncertainty in analytical experiments. Working at the intersection of cheminformatics, AI, machine learning, and analytical sciences, you’ll tackle two key challenges: representing chemical uncertainty in a computationally tractable way and creating the tools and data standards that enable scientists to make faster, more informed decisions. This is a two‑year fixed‑term position, offering the chance to work on a high‑impact project that addresses critical gaps in current analytical workflows and regulatory compliance.

Responsibilities

• Develop novel approaches for representing chemical uncertainty: In collaboration with leading academics (including Professor Jonathan Goodman at University of Cambridge) you will create computational methods and data standards that enable robust handling of incompletely characterised molecules in analytical workflows.

• Build AI‑driven systems for impurity tracking: Design and implement machine learning solutions that can track and link chemical observations across experiments as understanding evolves.

• Create tools that transform how analytical data is interpreted: Develop platforms that enable continuous learning, intelligent querying, and automated insights to accelerate decision‑making in drug development.

• Bridge computational prediction and experimental reality: Integrate predictive models with analytical data to refine understanding and validate approaches against real‑world pharmaceutical challenges.

• Collaborate across disciplines: Work with analytical chemists, data scientists, and pharmaceutical development teams to ensure your solutions address genuine scientific needs and integrate effectively.

• Drive adoption and share knowledge: Present your work to stakeholders, contribute to publications, and support scientists in leveraging new capabilities within their research.

• Contribute to the broader scientific community: Engage with academic collaborators and help advance how the field handles chemical uncertainty and analytical data.

Qualifications

• PhD in Cheminformatics, Computational Chemistry, Computer Science, Data Science, Machine Learning, Analytical Chemistry, or a related discipline, with a strong publication record and proven experience in at least one of: AI/machine learning development, cheminformatics, computational chemistry, or analytical data analysis.

• Hands‑on coding ability in Python (or similar languages), with experience applying computational methods to chemical or analytical problems.

• Understanding of either cheminformatics concepts and molecular representations, or analytical chemistry workflows and data interpretation (depth in one area is valued over breadth across all).

• Experience working with complex, real‑world datasets and an appreciation for handling uncertainty and incomplete information.

• Strong problem‑solving, teamwork, and communication skills, with the ability to bridge computational and experimental science.

Benefits

Competitive salary and benefits.

Company

This role offers the opportunity to develop innovative tools that will change how AstraZeneca handles impurity tracking and analytical data interpretation across the drug development pipeline. AstraZeneca’s Pharmaceutical Sciences function delivers the therapies of the future, accelerating molecules from idea to clinical reality through pioneering science, technological innovation, and digital transformation. The Digitisation team partners closely with scientists, engineers, and IT to create and embed robust, user‑centric informatics and automation solutions across the CMC spectrum, driving seamless data capture, integration, and scientific insight.