Ph.D. position on material sciences and machine learning

I am looking for a creative and self-motivated Ph.D. student to join the Department of Environmental Engineering Sciences at the University of Florida in Gainesville, FL, starting FALL of 2024. The project will focus on the application of machine learning techniques to advance the data-driven design of nanomaterials, with a particular focus on graphene, as part of a recently funded NSF CAREER. The students will leverage these techniques to create structured databases on graphene and use them to find useful correlations between synthesis conditions and output properties of the material.

We will start reviewing applications immediately and continue until the position is filled.

Candidate Requirements

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- Hold a bachelor's OR Master's degree in computer science, computer engineering, electrical engineering, data science, mathematics, statistics, econometrics, economics, operations research, or related field. Chemical, Environmental, or Mechanical Engineers or Material Scientists who have a background in programming are highly encouraged to apply.

- A master’s degree is desired but not required.

Desired skills

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- Proficiency in Python, R, or other modern languages/frameworks suitable for data science and machine learning.

- A solid foundation of statistics (hypothesis testing, outlier detection, statistical inference & analysis).

- Experience with machine learning and statistical methods (automated data annotation, feature selection, model training, correlation analyses, semantic analyses).

 

It would be great to see

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- Experience with natural language processing (NPL) packages such as NLTK and spaCy.

 

- Deep learning, large language models, generative AI.

 

-Experience with machine learning tools such as scikit-learn, TensorFlow, Torch, etc.

- Experience working on GNU/Linux platform (again, not required, but desired).

- Good interpersonal skills and ability to work on research tasks with limited supervision

- Excellent scientific writing and communication skills

We offer

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- Competitive stipend and benefits (tuition fully covered).

- Supportive research environment with lab space and equipment.

- Multidisciplinary collaboration with chemists, computer scientists, and material scientists in industry and academia.

- Professional development opportunities, including travel to conferences and workshops, leadership development, and career guidance

How to apply

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Candidates are encouraged to contact Dr. Andreia Faria (Email: andreia.faria@essie.ufl.edu) with a subject line as “Ph.D. Application - Your Name.” In addition, please send a single file including:

1. Curriculum vitae.

2. Unofficial transcripts.

3. Contact information for 3 references (including names, affiliations, and emails).

4. All documents must be in English.