Nanoscale Interface Modeller for Energy Storage

BCAM - Basque Center for Applied Mathematics; España; On-site; PHP 2,025,000 - 2,603,000; Full time

Benefits offered by this job

Mobility allowance €710/month

Family allowance €660/month

Tax exemption up to 30% in Bizkaia

Job summary

BCAM - Basque Center for Applied Mathematics in Bilbao, Spain, seeks a Early-Stage Researcher (PhD) to develop inverse modelling and optimization techniques for detailed 3D representations of complex interfaces in energy storage and conversion devices at the atomistic level.

The project will leverage Bayesian sampling, MC schemes, and adaptive integration, integrating spectroscopy data to generate candidate structures refined by high-resolution microscopy and NMR measurements.

Qualifications

• Experience with atomistic or particle simulations: Molecular Dynamics, Monte Carlo, and free energy methods.
• Strong knowledge of Bayesian inference, data science and machine learning.
• Experience in scientific computing in C++, Python, or Julia.
• Knowledge of electrochemistry and materials science is desirable.
• Ability to work in an international, multi-disciplinary team.

Responsibilities

• Develop inverse modelling and optimization techniques to generate 3D representations of interfaces.
• Integrate spectroscopy data (XPS, SAX, SXRD) to generate candidate structures.
• Refine models using high-resolution microscopy and NMR measurements.
• Collaborate within an international, multi-disciplinary team.
• Analyze interfacial ionic transport and structural evolution during electrochemical processes.

Skills

Molecular Dynamics

Monte Carlo

Free energy methods

Bayesian inference

Data science

Machine learning

C++

Python

Julia

Electrochemistry

Materials science

Teamwork

Education

Master's degree in Applied Mathematics, Physics, Computational Chemistry or closely related disciplines

Tools

C++

Python

Julia

Job description

BCAM - Basque Center for Applied Mathematics in Bilbao, Spain, seeks a Early-Stage Researcher (PhD) to develop inverse modelling and optimization techniques for detailed 3D representations of complex interfaces in energy storage and conversion devices at the atomistic level.

The project will leverage Bayesian sampling, MC schemes, and adaptive integration, integrating spectroscopy data to generate candidate structures refined by high-resolution microscopy and NMR measurements.