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MONASH UNIVERSITY

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PhD in Generative AI, Molecular Dynamics, and DFT for Organic Semiconductor Discovery Monash University in Australia

Degree Level

PhD

Field of study

Computer Science

Funding

3.5-year fixed-term PhD appointment with an AUD $37,145 per annum tax-free stipend at the 2026 full-time rate (pro-rata). The program includes a fully funded 12-month research stay at the University of Bayreuth as part of the joint degree.

Deadline

Nov 30, 2026

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Country

Australia

University

MONASH UNIVERSITY

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Keywords

Computer Science
Chemistry
Materials Science
Computational Chemistry
Molecular Dynamics
Organic Electronics
Density Functional Theory
Electronic Properties
Dft
Physics

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About this position

Monash University is advertising a double PhD degree opportunity with the University of Bayreuth for a project on accelerating organic semiconductor discovery with generative AI, molecular dynamics, and density functional theory (DFT). The research sits at the intersection of materials science, computational chemistry, scientific machine learning, and physics, with a focus on designing next-generation organic semiconductor materials with tailored optoelectronic properties.

The program is part of the Monash–Bayreuth Joint PhD Program and the DFG-funded IRTG 2818 / OPTEXC framework. Successful candidates will complete a 3.5-year doctoral program, including an extended research stay of 6 to 12 months at the partner institution in Germany. The post highlights a fully funded 12-month research stay at the University of Bayreuth and a tax-free stipend of AUD $37,145 per annum at the 2026 full-time rate.

Applicants should have, or be close to completing, a Bachelor Honours degree (H1 or equivalent) and/or a Master's degree with an excellent academic record. Candidates finishing a 4-year Honours degree or equivalent 3+1 program with a final-year research project and H1 standard may also be considered. The post specifically seeks applicants with experience in computational chemistry, materials simulation, scientific machine learning / AI, molecular dynamics, DFT, or a related materials science background. Strong English communication, problem-solving ability, and the capacity to work independently and in a multidisciplinary team are emphasized.

To apply, send a CV and cover letter describing your suitability to Dr Swarit Dwivedi at [email protected]. Applications close on 30 November 2026 at 11:55 pm AEDT, though the position may be filled earlier and applications are reviewed as they are received.

Funding details

3.5-year fixed-term PhD appointment with an AUD $37,145 per annum tax-free stipend at the 2026 full-time rate (pro-rata). The program includes a fully funded 12-month research stay at the University of Bayreuth as part of the joint degree.

What's required

Applicants should hold, or be close to completing, a Bachelor Honours degree (H1 or equivalent) and/or a Master's degree with an excellent academic record. Candidates completing a 4-year Honours degree or equivalent 3+1 program with a final-year research project aiming for or achieving H1 standard will also be considered. Background or experience in computational chemistry, materials simulation, scientific machine learning/AI, molecular dynamics or DFT, or materials science is preferred. Applicants must have strong written and verbal English communication skills, strong problem-solving and communication skills, and the ability to work independently and in a multidisciplinary research team. Applicants who already hold a PhD are not eligible.

How to apply

Email your CV and a cover letter explaining your suitability to Dr Swarit Dwivedi. Applications are reviewed as received, so early submission is encouraged. Apply via the listed Monash careers page if needed.

More information can be found here

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