Industrial PhD student in Machine Learning and Drug Design in Molecular AI

Project: Development of machine-learning methods to improve binding-affinity estimates

We are looking for an Industrial PhD student in Computational Drug Development to work on a project in a collaboration involving AstraZeneca, Molecular AI, Discovery Science, Gothenburg, Sweden, Division of Computational Chemistry, Lund University and the Foundations of Machine Learning group at KTH. This position is funded by DDLS (Data‑Driven Life Science / SciLifeLab).

The project: The industrial PhD student will focus on developing precision ML/AI models for rational small-molecule drug design. The overarching objective is to build physics-informed machine learning models for binding affinity prediction, enabling generative models to design chemically plausible compounds and prioritize candidates from hit discovery through to lead optimization. To achieve this, the PhD student will apply a range of computational approaches, incorporating fundamental features derived from physics-based methods, and integrating them with start-of-the-art machine learning techniques. This combined strategy aims to enhance predictive accuracy and efficiency and improve the quality of next generation drug candidates.

Focus areas of the position: Drug design, Machine learning, Binding affinity prediction, Computation Chemistry

Essential skills and experience:

• Master of Science in Machine Learning, Computer Science, Mathematics, Statistics, Physics, Computational chemistry, Bioinformatics or related discipline.

• Excellent written and verbal communications skills in English.

• Highly collaborative mindset and strong motivation working in this field.

• Willingness to engage with cutting-edge machine learning methods and foundational principles, as well as computational molecular modelling approaches.

• Ability to rapidly acquire the necessary skills and training.

Desirable skills and experience:

• Experience with small molecule binding affinity prediction.

• Proficiency in Python and modern deep learning (e.g., transformers, diffusion, GNNs, generative models) applied to scientific problems.

• Practical experience with molecular modelling methods, including Molecular Mechanics, Molecular Dynamics, and Quantum Mechanics for small molecules.

• Preliminary experience with computational workflow tools and computing environments required for computational modelling, machine learning, and high-performance computing.

• Exposure to workflow automation and collaborative software development practices.

• Previous experience of any of the core subjects, industrial exposure and collaboration and entrepreneurial skills are meritorious.

• Courses in quantum mechanics, statistical mechanics, medicinal chemistry and bioinformatics.

The research group: The research time of the PhD student will be split between Lund University and AstraZeneca, Gothenburg.

AstraZeneca is a global, science-driven biopharmaceutical company. We are dedicated to turning ideas into life-changing medicines and strive to continuously meet the unmet needs of patients worldwide. At AstraZeneca R&D in Gothenburg we have more than 3000 people from over 50 countries to support generation of new medicines through drug discovery, development, and clinical trials.

Molecular AIat AstraZeneca is passionate about innovation and transforming the way we do drug design using AI to accelerate the search for new medicines. We are doing it through pioneering scientific research and productionalization of AI methods and in partnerships with drug designers across the whole AstraZeneca portfolio of synthetic modalities. You will be supervised by Dr. Lili Cao and Dr. Jon Paul Janet and join the Molecular Design Team, a cross-disciplinary team of experts in machine learning and computational chemistry that includes multiple PhD students and postdoctoral researchers. This collaborative environment fosters knowledge sharing and provides valuable exposure to the industrial drug discovery process.

More information at: https://www.astrazeneca.com/

Lund University was founded in 1666 and is repeatedly ranked among the world’s top universities. The University has around 47 000 students and more than 8 800 staff based in Lund, Helsingborg and Malmö. We are united in our efforts to understand, explain and improve our world and the human condition. Lund University welcomes applicants with diverse backgrounds and experiences. We regard gender equality and diversity as a strength and an asset. The Division of Computational Chemistry involves ten research groups in all areas of quantum mechanics and statistical mechanics, performing method development and applications in biochemistry, medicinal chemistry, physical chemistry and intermolecular interactions.

At Lund University, the PhD student will be linked to the Computational Biochemistry group of Prof. Ulf Ryde. The group takes a multidisciplinary approach and combines quantum chemistry, statistical mechanics and machine learning with biochemistry, medicinal chemistry and structural biology to understand structure and function of biological macromolecules and to manipulate their function, e.g. by drug development.

More information at: https://www.compchem.lu.se/ulf-ryde

KTH Royal Institute of Technology in Stockholm is Sweden’s leading technical university with a vibrant community dedicated to research and advancing sciences. Since its founding in 1827, KTH has been at the forefront of innovation, consistently ranking among the top technical universities globally. Our Foundations of Machine Learning research group at KTH sits at the intersection of theoretical computer science, mathematics, and machine learning, focusing on the development of trustworthy machine learning methods, novel data analysis approaches, and efficient algorithms that solve complex real-world challenges with formal rigor.

To support your work at Lund University and AstraZeneca in developing cutting-edge machine learning methods, you will additionally be affiliated with the Foundations of Machine Learning group at KTH Royal Institute of Technology, led by Assistant Professor Sebastian Dalleiger. You will have the opportunity to engage with an active community of local machine learning researchers, participate in seminars as well as scientific discussions, and benefit from interdisciplinary collaboration across theory and applications. The lab conducts research spanning geometry, graph learning, transformers, diffusion models, causal learning, clustering, recommenders, graph algorithms, uncertainty quantification, robustness, and physics-informed machine learning.

More information at: https://www.kth.se/en

How to apply: The application, written in English, is submitted through the application portal of AstraZeneca by April 19th, 2026 and should contain:

• Cover letter in which the applicant motivates their interest for the position and states relevant qualifications.

• CV including at least two references (phone number and email)

• Copies of relevant certificates, degrees and grades.

Pay: According to local agreement

Start date: 2026-09-01 or otherwise agreed.

Type of employment: Temporary position, 4 years

Working hours: 100%

Date Posted

31-mars-2026

Closing Date

19-apr.-2026

Our mission is to build an inclusive and equitable environment. We want people to feel they belong at AstraZeneca and Alexion, starting with our recruitment process. We welcome and consider applications from all qualified candidates, regardless of characteristics. We offer reasonable adjustments/accommodations to help all candidates to perform at their best. If you have a need for any adjustments/accommodations, please complete the section in the application form.

AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorisation and employment eligibility verification requirements.