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Master Thesis Opportunities

We offer opportunities for master's and doctoral thesis research collaborations. If you are a student seeking to explore research opportunities or collaborate on a thesis, we would be happy to discuss potential projects.

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Computational Chemistry

1. Multiscale Modeling of Biomaterials: Bridging Microscale to Macroscale

Project Overview

This project focuses on the multiscale modeling of biomaterials, bridging molecular interactions at the microscale to mechanical and structural behaviors at the macroscale. The work will involve developing and applying computational models to understand and predict the properties of biomaterials for applications in tissue engineering and regenerative medicine.

Key Objectives

Develop multiscale models to simulate biomaterial behavior across scales. Analyze the impact of molecular interactions on material properties. Validate simulations through comparison with experimental data.

Skills You’ll Develop

Expertise in molecular and continuum modeling approaches. Proficiency in multiscale simulation frameworks. Interdisciplinary collaboration with experimental scientists.

Who Should Apply?

Master’s or Ph.D. candidates with a background in:

  • Computational physics, chemistry, materials science, or related fields

  • Interest in programming and/or simulation experience (e.g., Python, MATLAB, Amber, LAMMPS, GROMACS, OpenMM).  

  • Interest in biomaterials and multiscale modeling.

2. Force Field Parametrization for new molecular entities

Project Overview

Join an exciting research initiative focused on force field development and parametrization for accurate molecular simulations. This project aims to refine existing models or develop new parameters for biomolecules, materials, or drug candidates, enhancing predictive capabilities in computational chemistry.

Key Objectives

  • Parametrize new force field parameters for challenging biomolecules or materials. (Non-conventional amino acids, small molecular entities such as  biotin, cyanine dyes, etc.)  

  • Validate parameters through molecular simulations and experimental data comparisons.  

  • Improve the accuracy and efficiency of computational workflows. 

Skills You’ll Develop

  • Expertise in force field development (e.g., AMBER, GROMOS, MARTINI).  

  • Molecular dynamics simulations and model validation.  

  • Proficiency in scripting and automation for parametrization tasks. 

Who Should Apply?

Master’s or PhD candidates with a background in:

  • Computational chemistry, physics, materials science or related fields  

  • Interest and/or experience with force fields and simulation software (e.g., GROMACS, LAMMPS). 

  • Good analytical and coding skills (e.g., Python, C++)   

AI applications to Drug Discovery

1. AI-Driven Design of Peptides and Antiviral Drugs

Project Overview

This project leverages artificial intelligence and molecular dynamics simulations to design innovative peptides and small molecules with antiviral activity. These will eventually be included as functional motifs of self-assembling peptide systems. We aim to accelerate the discovery of effective therapeutics against emerging viruses by integrating computational tools and data-driven approaches.

Key Objectives

  • Use AI to design peptides and small molecules with antiviral potential.  

  • Perform molecular dynamics simulations to refine and validate designs.  

  • Collaborate with experimental teams for synthesis and testing. 

Skills You’ll Develop

  • Expertise in AI frameworks for drug design. (RFdiffusion + GROMACS, OpenMM)  

  • Proficiency in molecular dynamics and virtual screening.  

  • Knowledge of antiviral mechanisms and therapeutic development.

Who Should Apply?

  • Master’s or PhD candidates with a background in:  

  • Computational chemistry, drug design, biophysics or related fields

  • Interest and/or experience with machine learning and molecular simulations (e.g., Amber, GROMACS, OpenMM, LAMMPS, PyTorch).  

  • Interest in antiviral drug discovery and peptide therapeutics.  

Biomedical Informatics

1. Innovations in Biomedical Informatics for Healthcare Solutions

Project Overview

Explore the intersection of data science and healthcare through biomedical informatics. This project involves developing computational tools and workflows for analyzing biological data, integrating clinical and omics datasets, and driving innovations in biomedical research.

Key Objectives

  • Develop computational pipelines for integrating and analyzing biomedical datasets.  

  • Apply machine learning to identify patterns in pre-clinical and omics data.  

  • Contribute to the development of decision-support tools for medical research and healthcare.

Skills You’ll Develop

  • Expertise in data integration and analysis in biomedical informatics.  

  • Proficiency in machine learning and statistical modeling for biomedical research and healthcare applications.  

  • Knowledge of omics technologies and biomedical research, pre-clinical data systems.

Who Should Apply?

Master’s or PhD candidates with a background in:  

  • Biomedical informatics, bioinformatics, computational biology or related fields  

  • Programming skills (e.g., Python, R, SQL).  

  • Interest in healthcare innovation and personalized medicine.

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