dc-26057 – postdoctoral researcher in data-driven and mechanistic modelling for chemical safety

Temporary contract | 24 months | Belval
Are you passionate about research? So are we Come and join us

The Luxembourg Institute of Science and Technology (LIST) is a Research and Technology Organization (RTO) active in the fields of materials, environment and IT. By transforming scientific knowledge into technologies, smart data and tools, LIST empowers citizens in their choices, public authorities in their decisions and businesses in their strategies.

Do you want to know more about LIST? Check our website:

How will you contribute?

• The successful candidate is expected to:
• Develop mechanistic and data-driven models to analyse concentration–response relationships and biological readouts from advanced in vitro assays
• Apply statistical and computational methods to quantify uncertainty, robustness, and reproducibility in experimental data
• Contribute to feature selection and model interpretability, supporting biologically and regulatory-meaningful decision making
• Collaborate closely with experimental scientists to translate complex computational results into actionable insights
• Support the integration of modelling results into reproducible, FAIR-compliant data workflows
• Contribute to scientific publications, project deliverables, and dissemination activities in collaboration with academic and industrial partners

Is Your profile described below? Are you our future colleague? Apply now

Education

• PhD in a quantitative scientific discipline such as computational chemistry, chemical engineering, physics, materials science, applied mathematics, or a closely related field
• PhD training covering topics such as computational modelling, numerical methods, statistical analysis, machine learning or data-driven analysis of complex systems

Experience

• 0–3 years of postdoctoral experience (or equivalent research experience) in computational, statistical, or data-driven modelling
• Experience working with complex scientific datasets, either from simulations, experiments, or a combination of both
• Demonstrated ability to bridge data-driven approaches with mechanistic understanding
• Prior experience in life sciences, toxicology, or regulatory science is an advantage but not required

Hard skills

• Strong background in computational and/or mechanistic modelling of physical, chemical, or biological systems (e.g., PBPK, molecular dynamics)
• Experience with cheminformatics libraries and chemical datasets is an advantage
• Experience with statistical analysis, regression, or uncertainty-aware modelling
• Proficiency in scientific programming, particularly in Python (experience with scientific computing and ML libraries is a strong asset)
• Familiarity with high-dimensional data analysis, feature selection, or dimensionality reduction techniques
• Experience developing reproducible computational workflows (e.g. version control, documentation, automation)

Soft skills

• Ability to work effectively in interdisciplinary teams spanning computational and experimental research
• Strong analytical and problem-solving skills, with attention to scientific rigor and interpretability
• Experience with XAI tools (SHAP, LIME, Integrated Gradients) to identify which features of the model are driving the predictions
• Clear written and verbal communication skills, including the ability to explain complex concepts to non-specialists
• Intellectual curiosity and motivation to work on applied, impact-driven research
• Ability to work independently while contributing constructively to a collaborative project environment

Language skills

• Fluency in English both oral and written. Other relevant languages are an asset.

Your LIST benefits

• An organization with a passion for impact and strong RDI partnerships in Luxembourg and Europe that works on responsible and independent research projects
• Sustainable by design, empowering our belief that we play an essential role in paving the way to a green society
• Innovative infrastructures and exceptional labs occupying more than 5,000 square meters, including innovations in all that we do
• An environment encouraging curiosity, innovation and entrepreneurship in all areas
• Personalized learning programme to foster our staff's soft and technical skills
• Multicultural and international work environment with more than 50 nationalities represented in our workforce
• Diverse and inclusive work environment empowering our people to fulfill their personal and professional ambitions
• Gender-friendly environment with multiple actions to attract, develop and retain women in science
• 32 days' paid annual leave, 11 public holidays, 13-month salary, statutory health insurance
• Flexible working hours, home working policy and access to lunch vouchers

Apply online

Your application must include:

• A motivation letter oriented towards the position and detailing your experience
• A scientific CV with contact details
• List of publications (and patents, if applicable)
• Contact details of 2 references

Please apply ONLINE formally through the HR system. Applications by email will not be considered.

Application procedure and conditions

We kindly request applicants to provide their nationality for statistical purposes only, as part of our commitment to promoting diversity and ensuring equal opportunities in our workforce. This information will be kept confidential and will not be used for any discriminatory purposes.

LIST is dedicated to maintaining an inclusive work environment and is an equal opportunity employer. We are committed to attracting, hiring, and retaining a diverse workforce. All applicants will be considered for employment without discrimination based on national origin, race, colour, gender, sexual orientation, gender identity, marital status, religion, age, or disability.

Applications will be continuously reviewed until the position is filled. An assessment committee will thoroughly evaluate applications, adhering to guidelines designed to ensure equal opportunities. The primary criteria for selection will be the alignment of the applicant's existing skills and expertise with the requirements mentioned above.