Karlstad University
jobid=A.0.0503
Assistant Professor in Artificial Intelligence and Structure-Based Drug Discovery
Leiden University was founded in 1575 and is one of Europe’s leading international research universities.
The Faculty of Science and the Leiden Academic Center for Drug Research are looking for a Assistant Professor in Artificial Intelligence and Structure-Based Drug Discovery (1.0 fte).
Background
Computational methods have grown to be an integral part of drug discovery over the last three decades. Prior to the ongoing AI revolution, computer aided drug discovery (CADD) was already firmly embedded in the drug discovery process. Publicly accessible databases have emerged such as ChEMBL and DrugBank, which complement the available prior data sources such as the PDB. This has resulted in the availability of bioactivity data for a massive number of small molecules. Significant increases in computational power (e.g., development of graphics processor unit (GPU)-based computing) have gone hand in hand with the development of innovative algorithms like deep learning. These developments have sparked a renewed interest in the usage of molecular dynamics (MD) simulations to understand the dynamical processes that underlie ligand binding to proteins.
The vacancy is in the PI group Computational Drug Discovery (CDD) and in close collaboration with the national growth fund project Oncode Accelerator, specifically the AI platform. CDD works at the intersection of medicinal chemistry and computational sciences and applies novel AI algorithms to chemical and biological data.
Job Description
Educational program
At LACDR both the BSc program BioFarmaceutische Wetenschappen and the MSc program BioPharmaceutical Sciences (collectively BFW/BPS) are hosted. This position will contain teaching tasks in both curricula. The candidate is expected to develop novel courses, improve existing courses, and supervise internship students.
Research, using AI to improve structure-based methods
A part of the position is focused on research. At the division of Medicinal Chemistry, our interest is focused on understanding the interplay between ligands and proteins at an atomistic scale. Future and current research directions are expected to further integrate AI methods with MD simulations and the application of Free Energy Perturbation based methods.
Selection criteria
We are looking for a highly motivated candidate, who has:
- A PhD degree in Pharmaceutical Sciences, Informatics, Computational Chemistry, or related disciplines;
- Affinity with teaching;
- A strong interest and shown experience in machine learning and structure-based drug discovery;
- Experience in Python;
- Experience in polypharmacology modeling and proteochemometrics;
- Excellent proficiency in English;
- Collaborative mindset;
- Good technical skills;
- Comfortable working in a collaborative environment with initiative, creativity, and an independent working attitude.
Terms and conditions
We offer a position for four years (on the basis of 0.8 or 1.0 FTE). Salary ranges from €4,537 to €6,209 gross per month based on a full-time employment. Leiden University offers an attractive benefits package with additional holiday (8%) and end-of-year bonuses (8.3 %), training, and career development.
Diversity
Diversity and inclusion are core values of Leiden University. We are committed to becoming an inclusive community which enables all students and staff to feel valued and respected.
Applications
To apply for this vacancy, please submit your application online. Please ensure that you upload the following additional documents:
- A Cover Letter indicating motivation for the project;
- An updated CV, including a list of publications;
- A copy of your PhD thesis;
- A copy of your PhD degree;
- Contact details of two references.
Only applications received no later than March 7th, 2025 can be considered. The selection procedure will take place in March 2025, and selected candidates may be invited for a remote interview during this period.
Acquisition in response to this vacancy is not appreciated.
#J-18808-Ljbffr
