Publisher
source

Ulf Ryde

Top university

6 months ago

PhD in Machine Learning and Computational Chemistry for Binding Affinity Prediction Lund University in Sweden

Degree Level

PhD

Field of study

Computer Science

Funding

The position is a fixed-term full-time doctoral studentship at Lund University. Doctoral students are both admitted as students and employed by the university; the post runs for 4 years full time, with possible extension for teaching and other departmental duties. Salary is monthly, but no stipend amount is stated. The role is within Lund University and includes standard employee benefits.

Deadline

Expired

Country flag

Country

Sweden

University

Lund University

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Keywords

Computer Science
Machine Learning
Chemistry
Biology
Mathematics
Computational Chemistry
Molecular Modeling
Quantum Mechanics
Python Programming
Pharmacy
Statistical Mechanics
Bioinformatic
Physics

About this position

Doctoral student position in machine learning and computational chemistry at Lund University, in collaboration with AstraZeneca and Uppsala University.

The project focuses on binding affinity prediction for small molecules in drug design, combining physics-based methods and modern machine learning. Research topics include MM/PBSA, free-energy perturbation, SQM 2.20, fragment molecular orbital calculations, molecular modelling, quantum mechanics, statistical mechanics, and data-driven model development for prioritising drug candidates from hit discovery to lead optimisation.

The host environment is the Division of Computational Chemistry and the Computational Biochemistry group, which works across quantum chemistry, statistical mechanics, machine learning, biochemistry, medicinal chemistry, and structural biology. The project is tightly linked to Molecular AI at AstraZeneca and the Department of Pharmaceutical Biosciences at Uppsala University.

Eligibility highlights: applicants should have an MSc in computational chemistry, bioinformatics, machine learning, computer science, mathematics, statistics, physics, or a related discipline. Required skills include Python/programming, independent research ability, strong communication skills, good English, and a collaborative mindset. Preferred experience includes machine learning for scientific problems, molecular dynamics, molecular mechanics, quantum mechanics, binding affinity prediction, HPC, workflow tools, automation, and collaborative software development.

This is a full-time fixed-term doctoral studentship starting 2026-09-01 or as agreed. The employment is for 4 years full time, with extension possible for teaching and departmental duties. The post is based in Lund, Sweden.

Application materials must be in English and include a CV, cover letter, degree/study certificates, and any additional supporting documents such as transcripts, references, or recommendation letters. Deadline: 2026-04-26.

Funding details

The position is a fixed-term full-time doctoral studentship at Lund University. Doctoral students are both admitted as students and employed by the university; the post runs for 4 years full time, with possible extension for teaching and other departmental duties. Salary is monthly, but no stipend amount is stated. The role is within Lund University and includes standard employee benefits.

What's required

Applicants must meet general third-cycle admission requirements: a second-cycle qualification, or at least 240 credits with at least 60 credits at second-cycle level, or equivalent knowledge. Specific requirements include an MSc in computational chemistry, bioinformatics, machine learning, computer science, mathematics, statistics, physics, or a related discipline. Required skills include proficiency in programming such as Python, the ability to work independently and formulate research problems, strong written and oral communication skills, good English proficiency, and a highly collaborative mindset with strong motivation. Preferred qualifications include practical experience with modern machine-learning methods, molecular modelling (molecular mechanics, molecular dynamics, quantum mechanics), binding affinity prediction methods such as MM/PBSA and free-energy perturbation, computational workflow tools, HPC environments, workflow automation, collaborative software development, and coursework in quantum mechanics, statistical mechanics, medicinal chemistry, and bioinformatics.

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Professors

Professor

University Name
Country flag

Sweden

Research Interests

Pharmacy

Quantum Mechanics

Computer Science

Chemistry

Biology