• Identify existing machine learning approaches applicable for geochemical systems in a geological setting
• Implement, adapt, and apply machine learning approaches and mathematical surrogate models for modeling radionuclide migration in crystalline host rocks
• Perform uncertainty quantification of machine learning and surrogate models with respect to traceability, robustness and physicochemical correctness
• Cooperate with our project partners at HZDR, TU BA Freiberg and TU Darmstadt
• Present your scientific findings at academic venues and publish research in peer-reviewed journals

• Ph.D. in the field of Physics, Chemistry, Computer Sciences or Applied Mathematics or related field
• Background in mathematical surrogate modeling and machine learning
• Experience or familiarity with large-scale computational simulations
• Prior exposure to collaborative software development and version control systems (Git)
• Motivation to work collaboratively in a team-oriented environment
• Proficiency in programming languages (Python, Julia, C/C++)
• Excellent communication skills in English

• A vibrant research community in an open, diverse and international work environment
• Scientific excellence and extensive professional networking opportunities
• Salary and social benefits in accordance with the collective agreement for the public sector (TVöD-Bund) including 30 days of paid holiday leave, company pension scheme (VBL)
• We support a good work-life balance with the possibility of part-time employment, mobile working and flexible working hours
• Numerous company health management offerings
• Employee discounts with well-known providers via the platform Corporate Benefits
• An employer subsidy for the "Deutschland-Ticket Jobticket"

We look forward to receiving your application documents (including cover letter, CV, diplomas/transcripts, etc.), which you can submit via our online-application-system: https://www.hzdr.de/db/Cms?pNid=490&pLang=en&pOid=76987