Senior Researcher - AI-Enabled Computational Materials and Process Design

Technology Innovation Institute; Abu Dhabi Emirate; On-site; AED 250,000 - 450,000; Full time

Job summary

Technology Innovation Institute seeks a Senior Researcher to lead AI-enabled computational materials and process design for renewable energy tech. You will connect composition, structure, processing, and performance across atomistic to device scales.

The role combines physics-based simulation, data science, and AI to accelerate discovery of functional materials while collaborating with synthesis, characterization, and device teams.

Qualifications

• PhD in materials science, physics, chemistry or related field.
• 5+ years R&D in computational materials science or materials informatics.
• Experience translating research into practical decisions and deployment.
• Proven ability to build automated, reproducible computational workflows.
• Strong expertise in physics-based methods (DFT, MD, CALPHAD, etc.).
• Experience with AI for Materials including ML for prediction and optimization.
• Proficiency in Python and scientific software toolkits.

Responsibilities

• Lead computational materials and process design programs from problem definition through validation.
• Develop AI for Materials strategies combining physics models, data, and ML.
• Design and screen optoelectronic materials and metal alloys with performance targets.
• Apply multiscale modelling to predict composition-structure-property relationships.
• Create automated workflows for high-throughput calculation and design-space exploration.
• Collaborate with synthesis, characterization, and device teams to validate models.

Skills

Physics-based modelling

DFT

Machine learning

Python

MATLAB

Multiscale modelling

Opotoelectronic materials

Optoelectronic device physics

Materials informatics

Data analysis

Education

PhD in Materials Science

Physics or Chemistry or Chemical Engineering

R&D experience in academia or industry

Tools

Python

MATLAB

VASP

Quantum ESPRESSO

Gaussian

LAMMPS

Thermo-Calc

COMSOL

Lumerical

SCAPS

Job description

We are seeking a Senior Researcher to lead AI-enabled computational materials and process design for renewable energy technology. The role is centered on using physics-based simulation, data science, and artificial intelligence to discover and optimize functional optoelectronic materials and metal alloys, together with the processes that create their final structures and properties. The researcher will build integrated workflows that connect composition, molecular or atomic structure, processing history, microstructure, properties, and application performance. For optoelectronic materials, this requires a deep understanding of optical response and thin-film device physics so that device-level needs can be translated into meaningful material-design targets. Process modelling - including nucleation, crystallization, growth, phase evolution, and interfacial kinetics - will be used to predict how materials form and how processing choices influence performance.

We are looking for someone who is deeply curious about why materials and processes behave as they do and energized by turning that understanding into useful solutions. They will be self-motivated, entrepreneurial, and comfortable challenging assumptions, crossing disciplinary boundaries, and spotting connections that create new technical or commercial opportunities. They will welcome difficult problems, learn quickly, and persist through uncertainty while working constructively with others.

The successful candidate will combine scientific depth with a broad systems perspective. They will be able to identify the governing physics, select appropriate modelling methods, build reliable computational workflows, and convert simulation results into clear materials and process decisions. They should be comfortable moving between atomistic, mesoscale, and device-level questions while recognizing the assumptions and limitations of each model.

This is a collaborative research role. The Senior Researcher will work closely with synthesis, characterization, process, and device teams to define problems, validate predictions, and accelerate experimental learning. The role also requires initiative, intellectual curiosity, sound technical judgment, and the ability to communicate complex findings to both specialists and broader business stakeholders.

We will provide access to computational resources, experimental collaborators, and challenging materials-development programs in which high-quality modelling can directly influence technology direction.

Key Responsibilities

• Lead computational materials and process design programs from problem definition and model selection through candidate prioritization and experimental validation.
• Develop AI for Materials strategies that combine physics-based models, curated data, machine learning, and domain knowledge to accelerate materials discovery and optimization.
• Design and screen optoelectronic materials such as absorbers, emitters, dyes, charge-transport materials, photon-conversion materials, UV stabilizers, functional additives, and interlayers.
• Design and optimize metal alloys by evaluating composition, thermodynamic stability, phase behavior, defects, interfaces, microstructure, processability, and performance-relevant properties.
• Apply DFT, molecular and atomistic simulation, computational chemistry, thermodynamic and kinetic modelling, and multiscale methods to predict composition-structure-process-property relationships.
• Create automated, reproducible, and traceable workflows for high-throughput calculation, data generation, uncertainty assessment, candidate ranking, and design-space exploration.
• Develop or apply machine-learning methods for property prediction, inverse design, generative materials design, active learning, surrogate modelling, and multi-objective optimization.
• Translate optical and thin-film device requirements into quantitative material targets, screening metrics, and computational design rules.
• Use optical modelling to evaluate absorption, reflection, transmission, interference, optical-field distribution, light trapping, and parasitic losses in multilayer thin-film structures.
• Use thin-film device simulation to evaluate charge generation, transport, recombination, interfaces, EQE, current-voltage behavior, and the effect of material properties on device performance.
• Model material formation and evolution during processing, including nucleation, crystallization, crystal growth, solidification, phase transformation, and microstructure development.
• Determine how molecular or atomic interactions at interfaces control process kinetics, morphology, stability, and resulting material or device performance.
• Work with experimental teams to calibrate and validate models, recommend experiments, interpret discrepancies, and communicate outcomes through reports, publications, patents, and presentations.

Required Qualifications

• PhD in Materials Science, Physics, Chemistry, Chemical Engineering, Metallurgy, Electrical Engineering, Computational Materials Science, or a related discipline.
• At least 5 years of relevant R&D experience in computational materials science, materials design, process modelling, or materials informatics, preferably in an industrial environment, for example, within energy, optoelectronics, advanced manufacturing, or a related technology sector.
• Experience contributing to product or technology development, with an understanding of how research progresses from concept and validation through scale-up, qualification, and deployment.
• Demonstrated commitment to applying science to real-world solutions, with evidence of translating research or modelling insights into practical decisions, prototypes, processes, products, or measurable technology outcomes.
• Demonstrated success using computation to discover, screen, design, or optimize materials against defined technical objectives.
• Strong expertise in one or more physics-based materials methods, such as DFT, molecular dynamics, computational chemistry, Monte Carlo simulation, CALPHAD, phase-field modelling, or related multiscale approaches.
• Experience modelling composition-structure-property relationships in functional, semiconductor, molecular, metallic, or other advanced material systems.
• Practical experience with AI for Materials, including machine learning for property prediction, materials screening, inverse design, optimization, or scientific-data analysis.
• Strong scientific-programming skills and experience building automated, reproducible computational and data-analysis workflows.
• Deep knowledge of optical material properties, including electronic transitions, optical constants, absorption, emission, interference, and light management.
• Strong understanding of thin-film optoelectronic device physics, including interfaces, charge generation, transport, recombination, EQE, and current-voltage behavior.
• Ability to use optical and/or thin-film device models to define material requirements, compare candidates, and guide optoelectronic materials design.
• Understanding of process-structure-property relationships and the physical mechanisms governing nucleation, growth, crystallization, phase transformation, solidification, or microstructure evolution.
• Ability to integrate simulations with synthesis conditions, characterization results, process data, and device measurements.
• Experience with relevant tools such as Python, MATLAB, VASP, Quantum ESPRESSO, Gaussian, ORCA, LAMMPS, Thermo-Calc, COMSOL, Lumerical, SETFOS, SCAPS, or comparable platforms.
• Strong analytical judgment, including the ability to select appropriate model fidelity, quantify uncertainty, test assumptions, and recognize model limitations.
• Excellent communication, technical leadership, and collaboration skills, with the ability to work independently and deliver in a fast-paced multidisciplinary environment.

Preferred Qualifications

• Experience designing both optoelectronic materials and metal alloys; candidates with exceptional depth in one domain and clear evidence of transferable methods will also be considered.
• Experience with advanced AI for Materials approaches such as graph neural networks, generative models, materials foundation models, active learning, Bayesian optimization, or autonomous discovery workflows.
• Experience with optoelectronic materials or devices such as thin-film solar cells, indoor PV, organic semiconductors, perovskites, OLEDs, photodetectors, luminescent systems, or photon-management structures.
• Experience with computational alloy design, ICME, CALPHAD database use or development, phase-field modelling, solidification modelling, or microstructure-sensitive property prediction.
• Experience simulating nucleation, crystallization, crystal growth, or related processes and connecting process kinetics to molecular or atomic interactions at interfaces.
• Experience calibrating models with ellipsometry, UV-Vis, PL, EQE, reflectance, structural characterization, process-monitoring data, or electrical device measurements.
• A record of influential publications, patents, validated predictions, software or workflow development, or successful transfer of computational results into experiments or technology programs.