Research Associate position in computer-aided drug discovery

The Molecular Drug Design research group (Wolber group) focuses on computer-aided drug discovery for biologically relevant target structures. A particular emphasis is placed on in silico design, the modeling and analysis of protein-ligand interactions, as well as methods of virtual screening, molecular dynamics, and data-driven drug optimization. The developed models and predictions are validated in close collaboration with experimental research groups and applied to drug development challenges, especially in the area of ​​G-protein-coupled receptors and other pharmacologically relevant targets.

The research group plays a key role in teaching within the state examination program in pharmacy and the Master’s program in Pharmaceutical Research. It contributes to lectures, seminars, and practical courses, imparting knowledge in molecular modeling, computer-aided drug development, and structure-based drug research across various semesters.

We offer you modern working conditions in an interdisciplinary environment, a permanent position, an interesting and varied role with a high degree of autonomy, creative freedom and close collaboration with lecturers, students and cooperation partners, a diverse range of training opportunities for comprehensive onboarding as well as for professional and methodological development, work-life balance through flexible working time models, e.g. flextime and the possibility of mobile working, comprehensive company health management, 30 days of vacation per year as well as additional time off on December 24th and 31st, good transport connections and a subsidized company ticket.
 
Further information can be found at https://www.bcp.fu-berlin.de/pharmazie/index.html
 
Responsibilities:
• Computer-aided development and optimization of small molecule modulators at pharmacologically relevant target structures
• Structure- and ligand-based drug design, including pharmacophore modeling, docking, virtual screening, and machine learning-based or generative design methods
• Execution and evaluation of molecular dynamics simulations for the analysis of protein-ligand interactions and dynamic pharmacophores
• Close collaboration with experimental research groups for the validation and iterative optimization of computer-aided predictions
• Development, documentation, and maintenance of reusable scientific software workflows
• Participation in teaching in the state examination program in pharmacy and in the Master’s program
• Publication of results in peer-reviewed journals and participation in grant applications
• This position is intended for the candidate’s own academic qualification (doctoral studies).
 
Requirements:
Completed university degree (Master’s, Diploma, or equivalent) in a field relevant to the position, e.g., Interdisciplinary Natural Sciences, Computational Sciences, or a comparable science-oriented discipline.
 
Desired:
Experience in computational biology, machine learning/deep learning, or data-driven analysis of biological or pharmacological data. Strong programming skills, particularly in Python, are expected, as well as experience with scientific analysis environments under Linux/HPC. Experience with structural biology or pharmacological questions, protein-ligand modeling, multi-omics analyses, PK/PD modeling, clinical-pharmacological data analysis, or the development of reproducible scientific workflows is also desirable.
• Excellent knowledge of machine learning and deep learning, especially with PyTorch, TensorFlow/Keras, XGBoost, autoencoders, transfer learning, self-supervised learning, Gaussian processes, or related methods.
• Interest in structure- and ligand-based drug development, especially pharmacophore modeling, docking, molecular dynamics simulations, protein-ligand-related structural modeling, and computer-aided analysis of interaction patterns
. • Proven interest in neuropharmacological drugs, GPCRs or opioid receptors, functionally selective receptor modulation, and translational drug development
. • Experience at the interface of pharmacology, chemistry, biology, and computer science.
• Excellent written and spoken English skills as well as excellent German skills.
• Ability to work independently on scientific projects, methodological rigor, and a high degree of initiative
. • Enjoyment of interdisciplinary collaboration and excellent communication skills.
• Experience in the scientific presentation and publication of research results.
• Willingness to participate in university teaching.
• Eligibility for doctoral studies.
 

Further information

Applications, including all relevant documents and quoting the reference number , should be submitted electronically in PDF format (preferably as a single document) via email to Prof. Dr. Gerhard Wolber: gerhard.wolber@fu-berlin.de or by post to:
 
Freie Universität Berlin,
Department of Biology, Chemistry, Pharmacy,
Institute of Pharmacy,
Prof. Dr. Gerhard Wolber,
Königin-Luise-Str. 2 and 4,
14195 Berlin (Dahlem).
 
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