Doctoral student in Computational Chemistry Full-time

Doctoral Student In Computational Chemistry

Lund University was founded in 1666 and is repeatedly ranked among the world's top universities. The University has around 46,000 students and 8,500 staff based in Lund, Helsingborg and Malmö. We are united in our efforts to understand, explain and improve our world and the human condition.

Description Of The Workplace

The Division of Computational Chemistry involves ten research groups in all areas of quantum mechanics and statistical mechanics. The Computational Biochemistry group consists currently of eight coworkers 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. The project involves a tight collaboration with Molecular AI at AstraZeneca and the Department of Pharmaceutical Biosciences at Uppsala University.

Being A Doctoral Student

As a doctoral student, you are both admitted as a student and employed at Lund University. As a doctoral student, you will be trained in a scientific approach. In short, you will be trained to think critically and analytically, to solve problems independently using the right methods, and to develop an awareness of research ethics. In addition, you will have the opportunity to work on projects, to develop your leadership and pedagogical skills. Throughout your studies, you will be guided by supervisors. Doctoral studies end with a thesis and a doctoral degree.

Project Description

Calculating accurate estimates for the binding affinity of small molecules (e.g. drug candidates) to a macromolecular receptor (e.g. a protein) is one of the most important challenges of computational chemistry. The PhD student will develop physics-informed machine-learning models for binding affinity predictions in rational small-molecule drug design. The models will allow prioritisation of candidates from hit discovery through to lead optimisation. The project will start from physics-based methods like MM/PBSA, free-energy perturbation, SQM 2.20 and fragment molecular orbital calculations, and use start-of-the-art machine-learning techniques to improve the predictions and to select physical information important for the prodictions.

Work Duties

You will primarily devote yourself to your doctoral programme, which includes participation in research projects as well as third cycle courses, seminars and conferences. You will perform standard physics-based methods to estimate absolute and relative binding free energy. Based on these, you will develop machine-learning models to improve the estimated binding affinities. The duties may also include participation in teaching and other departmental work (however, a maximum of 20% of working hours).

Qualifications

To be eligible for admission and employment as a doctoral student, you must fulfil the requirements below.

Admission Requirements

A person meets the general admission requirements for third-cycle courses and study programmes if the applicant:

• has been awarded a second-cycle qualification, or
• has satisfied the requirements for courses comprising at least 240 credits of which at least 60 credits were awarded in the second cycle, or
• has acquired substantially equivalent knowledge in some other way in Sweden or abroad.

A person meets the specific admission requirements for third cycle studies in computational chemistry if the applicant has:

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

Additional Requirements

In order to complete the doctoral programme in question, the following are also required:

• Proficiency in programming, e.g. with Python.
• Good ability to work independently and to formulate and tackle research problems.
• Good written and oral communication skills.
• Good knowledge of English, spoken and written.
• Highly collaborative mindset and strong motivation working in this field

Other Qualifications

For the doctoral programme in question, the following are considered as other qualifications:

• Practical experience with modern machine-learning methods applied to scientific problems.
• Practical experience with molecular modelling methods, including molecular mechanics, molecular dynamics and quantum mechanics.
• Practical experience with small-molecule binding affinity prediction, e.g. MM/PBSA and free-energy perturbation.
• Experience with computational workflow tools and computing environments and high-performance computing.
• Exposure to workflow automation and collaborative software development practices.
• Courses in quantum mechanics, statistical mechanics, medicinal chemistry and bioinformatics.

We Offer

Lund University is a public authority which means that employees get particular benefits, generous annual leave and an advantageous occupational pension scheme.

About The Employment

The employment is a fixed-term employment at full time, starting 2026-09-01 or as agreed. Third cycle studies consist of full-time studies for 4 years. In the case of teaching and other departmental duties, the employment is extended accordingly. Doctoral studentships are regulated in the Higher Education Ordinance (1993:100), chapter 5, 1-7 §§.

How To Apply

Applications shall be written in English and include:

• CV and a cover letter stating the reasons why you are interested in the doctoral programme/employment and in what way the research project corresponds to your interests and educational background.
• Copies of issued study certificates and/or awarded degree certificates. These must confirm that you meet the general and specific admission requirements for the doctoral programme and show that you have the subject knowledge required for the doctoral programme project.
• Other documents you wish to be considered (grade transcripts, contact information for your references, letters of recommendation, etc.)

We welcome your application!