Job Description
Postdoc Fundamental Chemistry for Enabling Self-Driving Lab for Thermal CO₂ Valorization (m/f/d)
LOCATION

LOCATION

COMPANY

COMPANY

JOB FIELD

JOB FIELD


JOB TYPE


JOB TYPE

JOB ID


JOB ID


FLEXIBLE WORK OPTIONS


FLEXIBLE WORK OPTIONS

LOCATION

LOCATION


COMPANY

COMPANY


JOB FIELD

JOB FIELD


JOB TYPE

JOB TYPE


JOB ID

JOB ID


FLEXIBLE WORK OPTIONS

FLEXIBLE WORK OPTIONS

WELCOME TO BASF

We are inviting applications for a Postdoctoral Researcher within the ASCEND (Accelerate Positive Clean Energy Districts) project, contributing to Work Package 01 Digital Catalysis and Work Package 04 Self-Driving Lab for Thermal CO₂ Valorization.

WHAT YOU CAN EXPECT

The position focuses on the development of chemically meaningful descriptors and their on-demand computational generation, enabling their integration into data-driven experimental design frameworks. The overall objective is to support chemically informed and efficient exploration of large catalyst and process spaces within autonomous (self-driving) laboratory environments.

  • You will develop physically and chemically meaningful descriptors for catalytic systems relevant to thermal CO₂ valorization.
  • Furthermore, you will implement computational pipelines for scalable, on-demand descriptor generation.
  • You will interface descriptor frameworks with data-driven exploration strategies.
  • Additionally, you will contribute to method development and validation in collaboration with experimental and digital teams.
  • You will support the integration of methods into self-driving laboratory workflows.
  • Last but not least, you will disseminate results through scientific publications and project reports.

WHAT YOU OFFER

  • PhD in Chemistry, Chemical Engineering, Materials Science, Physics, or a related field
  • strong expertise in computational chemistry (e.g. DFT, atomistic simulations) and catalyst modelling (heterogeneous and/or homogeneous systems)
  • experience with Python-based scientific computing (e.g. FireWorks, Jobflow, pymatgen, ASE or similar tools) as well as modelling and analysis of catalytic or materials systems
  • proven ability to translate chemical insight into quantitative descriptors
  • knowledge of design of experiments (DOE), active learning, Bayesian optimization or machine learning methods for chemical systems is considered a plus
  • familiarity with high-throughput computational or experimental workflows, automated or self-driving laboratory concepts, as well as strong communication skills and the ability to work effectively in interdisciplinary teams

WHAT WE OFFER

  • Participation in a collaborative, interdisciplinary research project within ASCEND
  • Access to advanced computational and experimental infrastructures
  • Opportunity to contribute to the development of next-generation autonomous research systems
  • Dynamic research environment with strong academic and industrial collaboration

HOW TO REACH US

  • Larissa Anna Treiber (Talent Acquisition), larissa-anna.treiber@basf.com, Tel: +49 30 2005-56384 will be happy to answer your questions for this position.
  • You can also reach our recruiting team here.
  • First information about our application process can be found here.

ABOUT US

Please attach a curriculum vitae, a publication list, as well as a brief research statement (maximum two pages) describing your relevant experience and research interests (in English). For more information about the ASCEND project, please visit the ASCEND project website.

Diversity is our greatest strength!

Become a part of our winning formula for success and develop the future with us - in a global team that embraces inclusion and equal opportunities irrespective of gender, age, origin, sexual orientation, disability or belief.

Ludwigshafen am Rhein, DEU
BASF SE
Research & Development
Internship
142996
RnD
Germany
Work model:  On-site