Post Doc Research

Teknik, data och digitalt · Data, AI och analys · Datavetenskap · Maskininlärning

I korthet

Microsoft Research AI for Science is looking for a postdoctoral researcher in computational chemistry to contribute to the generation of data for Skala, a deep-learning exchange-correlation functional for DFT. The role involves designing and implementing methods for generating diverse molecular structures and building automated data-generation workflows in Python.

Ansvarsområden

Design, implement, and scale methods for generating chemically diverse molecular structures, including covalent, non-covalent, bond-dissociation paths, and reaction geometries/transition states.
Combine and extend structure-generation approaches like graph-based, fragment-based construction, conformer/reaction-path sampling, and molecular dynamics/metadynamics.
Define sampling and selection strategies to ensure new data fills gaps in chemical space and quantify overlap with existing datasets.
Build and maintain robust, automated, reproducible data-generation workflows in Python on shared codebases.
Collaborate with colleagues on data generation, model development, and deployment to inform priorities for future datasets.
Contribute to the efficiency of the data-generation pipeline, including exploring GPU-accelerated electronic-structure methods for speed-ups.
Prepare technical papers, presentations, and open-source releases of research code and data.

Krav

PhD in computational chemistry, theoretical chemistry, computational condensed-matter physics, or a related area, completed or close to completion (thesis submitted, or expected within 6 months of the start date), or comparable research experience.
Hands-on research experience with electronic-structure methods (DFT and/or semiempirical/tight-binding methods).
Hands-on research experience with generating, optimizing, or sampling molecular structures.
Proficiency in Python.
Experience with collaborative code development on shared codebases.
At least one peer-reviewed publication or preprint in computational chemistry or a related field.

Önskade kvalifikationer

Experience developing or contributing to open-source scientific software (e.g., quantum-chemistry or atomistic-simulation packages).
Experience with extended tight-binding (xTB/GFN) methods.
Experience with conformer, reaction-path, or molecular-dynamics-based structure exploration.
Experience building computational-chemistry datasets, for example for training machine-learning models.
Experience with GPU-accelerated or high-performance computing for quantum chemistry.
Familiarity with wavefunction-based methods (e.g., coupled cluster) and thermochemistry or reaction-energy benchmarks.
Experience integrating methods into existing quantum-chemistry codes (e.g., ORCA, PySCF, CP2K).

Förmåner

Access to substantial computational resources.
Results shared through publications and open-source releases.
Base pay: € 8,191.00 per month (Berlin)
Base pay: £ 6,655.00 per month (Cambridge)
Base pay: € 6,811.00 per month (Amsterdam)

Färdigheter

PythonDFTmolecular dynamicsmetadynamicstight-bindingxTBGFNGPU acceleration

#computational chemistry#deep-learning#data generation#DFT#quantum chemistry#machine learning#Python#research

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